ANALISIS TRIULANAN Peremangan Moneter Peranan dan Sitem ... Vol... · oil price help the US economy since the share of oil is large on US consumption basket. The development of labor

1ANALISIS TRIWULANAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan II - 2007

BULLETIN OF MONETARY ECONOMICS AND BANKING

Center for Central Banking Research and EducationBank Indonesia

PatronDewan Gubernur Bank Indonesia

Board of Editor

Prof. Dr. Anwar NasutionProf. Dr. Miranda S. Goeltom

Prof. Dr. InsukindroProf. Dr. Iwan Jaya Azis

Prof. Iftekhar HasanProf. Dr. Masaaki Komatsu

Dr. M. SyamsuddinDr. Perry Warjiyo

Dr. Iskandar Simorangkir Dr. Solikin M. JuhroDr. Haris Munandar

Dr. Andi M. Alfian ParewangiDr. M. Edhie Purnawan

Dr. Burhanuddin AbdullahDr. Andi M. Alfian Parewangi

Editorial Chairman

Dr. Perry Warjiyo

Managing EditorDr. Darsono

Dr. Siti AstiyahDr. Andi M. Alfian Parewangi

SecretariatIr. Triatmo Doriyanto, M.S

Nurhemi, S.E., M.ATri Subandoro, S.E

This bulletin is published by Bank Indonesia, Center for Central Banking Research and Education. Contents and results research in the writings in this bulletin entirely the responsibility of the authors and not an official view of Bank Indonesia.

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Quarterly Outlook on Monetary, Banking, and Payment System In Indonesia:

Quarter IV, 2014

TM. Arief Machmud, Syachman Perdymer, Muslimin Anwar, Nurkholisoh Ibnu Aman,

Tri Kurnia Ayu K, Anggita Cinditya Mutiara K, Illinia Ayudhia Riyadi

The Role of Currency Hedging on Firm Performance: A Panel Data Evidence in Indonesia

Fiskara Indawan, Sri Fitriani, Indriani Karlina, Melva Viva Grace

Risk Of Indonesian Banks: An Application of Historical Expected Shortfall Method

Nevi Danila, Bunyamin, Siti Munfaqiroh

Persistency and Sustainability of Indonesia’s Current Account Deficit

Tuti Eka Asmarani

The Effect of Ownership and Global Crisis to Income Diversification

of Indonesian Banking

Murharsito

BULLETIN of moNETary EcoNomIcsaNd BaNkINg

Volume 17, Number 3, January 2015

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271Quarterly Outlook On Monetary, Banking, And Payment System In Indonesia: Quarter IV, 2014

QUARTERLY OUTLOOK ON MONETARY, BANKING, AND PAYMENT SYSTEM IN

INDONESIA: QUARTER IV, 2014

TM. Arief Machmud, Syachman Perdymer, Muslimin Anwar, Nurkholisoh Ibnu Aman, Tri Kurnia Ayu K,

Anggita Cinditya Mutiara K, Illinia Ayudhia Riyadi1

1 Authors are researcher on Monetary and Economic Policy Department (DKEM). TM_Arief Machmud ([email protected]); Syachman Perdymer ([email protected]); Muslimin AAnwar ([email protected]); Nurkholisoh Ibnu Aman ([email protected]); Tri Kurnia Ayu K ([email protected]); Anggita Cinditya Mutiara K ([email protected]); Illinia Ayudhia Riyadi ([email protected]).

This paper provides current analysis of the ongoing quarter, and brief outlook on the forthcoming

quarter. We use a field survey along with estimation of macroeconomic models to provide the assessment

and to make some projections on the monetary, the banking, and the payment system in Indonesia. This

paper confirms a generally slowed down of Indonesian economy during 2014, even the domestic economy

recorded a growth from previous quarter. The macroeconomic and the financial system stability remains

strong particularly on Quarter 4, 2014 as reflected on the lower current account deficit and the maintained

inflation rate. The better performance of the current account during Quarter 4, 2014 was supported by

the surplus of the non-oil trade balance, and the reduction of oil trade deficit. On the other hand, the

financial system stability remains solid with the viability of banking system and moderate performance

of the financial market.

Abstract

Keywords: Macroeconomic, monetary, economic outlook.

JEL Classification: C53, E66, F01, F41

272 Bulletin of Monetary, Economics and Banking, Volume 17, Number 3, January 2015

I. GLOBAL DEVELOPMENT

The global economic recovery is predicted to continue although not uniformly across regions. We expect to see a higher growth of US economy relative to the previous outlook. On the other hand, the economy of Japan and China may be lower than initial expectation. The recovery in Europe may run slowly with the lower consumer’s confidence and the threat of deflation. This condition leads the European Central Bank (ECB) to lunch economic stimulus via Expanded Asset Purchase Program (EAPP). The monetary policy stimulus is expected to push the capital portfolio toward emerging market including Indonesia, along with its possibility to create uncertainty and volatility on global financial market.

The better economic growth in US relative to previous expectation is mainly due to their stronger domestic economy. The consumer’s confidence index in United States increases with the raise of the real income due to a lower oil price and development in labor sector. The lower oil price help the US economy since the share of oil is large on US consumption basket. The development of labor sector in United States is indicated from the reduction of unemployment to the rate of 5.6%, which is in line with the growth of new job openings (Figure 1).

The economic recovery in Europe is expected to run slowly. Several indicators confirm this trend, including the lower manufacturing production index (MPI) in Europe. The MPI in Italy and France are still in contractive phase, while the MPI in Germany and Spain started to expand. Along with this trend, the Europe entered the deflation zone since December 2014 (Figure 2).

To fasten the economic recovery in Europe, the monetary authority in Europe (ECB) launched quantitative easing via Enhanced Asset Purchasing Program (EAPP) amounted to 60 milliards Euro every month, starting from March 2015 until September 2016. The monetary stimulus is predicted to increase the global liquidity; even the predicted capital flow to Indonesia may not be as large as the impact of FED quantitative ease on last 2008.

Figure 1.Unemployment Rate and Job Opening AS

Figure 2.Inflation in Europe

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The economic growth in Japan may be lower than previously expected, since the growth of Japan’s domestic consumption is still weak. The retail sale in Japan is low and the consumer’s confidence recorded a declining trend. On the other hand, the labor sector remains constant without significant support to increase demand. The level of the job hires is in declining trend and the growth of wage is still weak.

On the other side, the quantitative easing by the Bank of Japan positively depreciates the Yen, which support the Japan’s export. Ahead, the performance of Japan’s economy is predicted to increase with several support of government policies including the fiscal stimulus of 3.5 trillion Yen, tax reduction incentive for corporation that increase their employee’s salary, and also the structural reform (Abe’s 3rd arrow).

The China’s economy is predicted to grow slower than previously expected. The slowing down of Chinas economy is due to the declining trend of investment as indicated by decrease of fixed investment indicator (Figure 3). Beside investment, the consumption (proxied by retail sales) is also in declining trend. From production side, the production index decrease and the MPI also decrease and entering contractive zone. The slowing down likelihood of China’s economy is also reflected in the large downside risk of housing sector (the price of property is low), and is also reflected by the increasing of deleveraging risk.

In India, the economy is predicted to grow in lower rate than previous outlook. This is in line with the weak of external demand, particularly from China. However, the increase of India’s term of trade due to the oil price cut has compensated the negative impact of this weaker external demand. On the other hand, the investment activity and industry has increased post the implementation of policy reforms. This is clearly reflected in the increase of Manufacturing Production Index and business confidence (Figure 4).

Figure 3. The Development ofInvestment and Consumption in China

Figure 4.The Business Confidence in India

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Within the global financial market, potential uncertainty and volatility may arise along with the plan of European Central Bank to launch monetary stimulus. Rumors and uncertainties on the implementation of EAPP arise because it involves many central banks in Eurozone, plus the political uncertainty in certain country such as Greek. The weakening of economic growth in European country members also contributes to these uncertainties, since the excess liquidity in Europe will likely flow to US with higher yield. In the end, the additional liquidity in US will appreciate USD; hence increase the global financial market volatility.

II. THE DYNAMICS OF MACROECONOMY IN Indonesia

2.1. Economic Growth

The economic growth of Indonesia in Quarter 4, 2014 increased compared to previous quarter even though it slowed down during the whole 2014 in general. The growth was 5.01% (yoy) during Quarter 4, 2014, slightly higher than Quarter 3 of 4.92% (yoy), see Table 1. This is a clear signal to the market where the economic slowing down during the last several years has reached its lowest point in last quarter. The better economic growth is supported mainly by the increase of domestic demand, particularly on property investment and government consumption. On the other hand, the household consumption remains strong even with slight lower growth due to the economic stabilization policy.

On external side, the export performance recorded significant contraction, particularly due to the weakening of demand from emerging countries and also due to the reduction of commodity prices. Regionally, the slowing economic growth occurred in natural resource-based regions including Province of Aceh, East Kalimantan, Riau, and Papua. Contrarily, those regions rely more on manufacturing remains strong.

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Figure 5.World Trade Volume

Figure 6.The Growth of Non-Oil Export, Real

Regardless of its strength, the household consumption during Quarter IV, 2014 recorded declining trend along with the macroeconomic stabilization policy. The growth of household consumption declined from 5.08% (yoy) in Quarter III, 2014 to 5.01% (yoy) in Quarter IV, 2016. The main reason behind this is the weakening purchasing power because of inflation triggered by the fuel price increase and slower income growth from export. Furthermore, the consumption was also restrained by the macroeconomic stabilization policy. The lower purchasing power is clearly reflected from the declining of consumer confidence on economic condition and the weakening of their income expectation.

The slower household consumption is also reflected on sale contraction of car and vehicle during Quarter IV, 2014. In contrast with the household consumption, the government consumption grew higher this quarter. The government consumption grew by 2.83% (yoy), relatively higher than the growth in previous quarter, 1.33% (yoy). The increase of government consumption was mainly driven by the routine personnel expenditure.

The performance of investment increased supported by the higher growth of property investment. The investment grew gradually from 3.86% (yoy) on Quarter III, 2014 to 4.27% (yoy) on Quarter IV, 2014. The growth of investment was mainly arise from property sector that grew by 7.06% (yoy), which is in line with the increase of investment post the national election. The growth of property sector is clearly indicated from the stable growth of cement sales. Contrarily, the growth of non-property sector investment contracted along with the slowing household consumption and contracted export. The contraction on non-property sector was indicated on lower capital import and domestic sales of heavy equipment during Quarter IV, 2014.

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2 The published GDP by BPS on February 5, 2015 use year base of 2010 and use Social Accounting Matrix of 2008. The latter distinguish and classify the economy to 17 sectors (previously only 9 sectors)..

On external performance, the export decline because of weaker demand from emerging countries and the decline of commodity prices. Export on Quarter IV, 2014 recorded a contraction of 4.53% (yoy), which is far below the positive growth of 4.86% (yoy) on Quarter III, 2014. In addition, the export contraction is also affected by the base effect of high mining export on Quarter IV, 2013 prior the implementation of UU Minerba on early 2014 (Figure 6). Regardless the declining of overall export during 2014, the manufacturing sector still recorded a positive performance following the recovery of US economy and development of Rupiah around its fundamental.

In Quarter IV, 2014, the import increased in response to the growth increase of domestic demand by 3.22% (yoy) which is higher than previous quarter of 0.28% (yoy). From non-oil sector, the raw material import increased to fulfill the domestic industry demand. The higher growth of import was also affected by the policy of Pertamina to anticipate the raise of consumption following the plan to increase the price of subsidized fuel on the mid of November 2014.

On Quarter IV, 2014, the performance of economy increased in several sectors2 particularly the non-tradable sectors. The positive performance of non-tradable sectors was mainly on property sector following the optimism of the new government. On the other side, the performance of tradable sector declined relative to previous quarter. The agriculture grew slower because the harvesting for Paddy declined and is substituted to Maize and Soy. On the other hand, the performance of industry sector also declines since the private consumption is lower as well as the export by the end of 2014.

Across regions, the recovery of national economy on Quarter IV, 2014 is from Java (Picture 1). This is mainly because of the manufacturing sector activity in Java regions. The economic recovery also occurs in Kalimantan with their mining activities, even though the effect is still limited due to the low price of the commodities. In Sumatera, the plantation activities have helped this region to recover and to grow steadily. On eastern Indonesia, the economic recovery run slowly due to the contraction of mining sector on this regions including Papua, Papua Barat, Sulawesi Tenggara, and Sulawesi Tengah.


2.2. Balance of Payment

The Balance of Payment performance in Indonesia increased during Quarter IV, 2014; mainly supported by the declining of current account deficit. Overall, the balance of payment on this quarter recorded a surplus of USD 2.4 milliard (Figure 7). The surplus is derived from the lower current account deficit and the large surplus of financial and capital account. The performance of BOP has in turn increase the national reserve from USD111.2 milliard on Quarter III, 2014 to USD111.9 milliard in Quarter IV, 2014 (Figure 8). This national reserve is sufficient to finance national import and government foreign debt during 6.4 months ahead; this is above the average international standard. On January 2015, the foreign exchange reserve increase more to USD114.2 milliard.

Amidst the slower process of global economic recovery compared to the initial prediction, the performance of Indonesia’s current account increased. The current account deficit on Quarter IV, 2014 was USD6.2 milliard (2.81 percent of GDP);lower than the previous deficit of USD7.0 milliard (2.99% of GDP). Better performance of the current account is supported by the increase of trade balance surplus particularly from non-oil sectors, and the deficit reduction of the oil sectors. The trade surplus of non-oil sectors includes the manufacturing and vegetable oil. For oil sectors, even though their trade volume increase the trade deficit still increase because the drop of crude oil price. On January 2015, the trade balance recorded a surplus of USD 0.7 milliard, which is higher than previous month.

Picture 1.The Map of Economic Growth across Regions in Indonesia, Quarter IV, 2014

gPDRB > 7% 5% < gPDRB < 6% 4% < gPDRB < 5% gPDRB Negative6% < gPDRB < 7% gPDRB < 4%

ACEH1

SUMUT4.8

RIAU1.1

KEP. BABEL4.8

DKI JAKARTA5.7 JATENG

6.2

SULTENG9.5

KALTIM3.8

KALBAR3.9

SULUT6.1

MALUT5.2

PAPBAR0.09

PAPUA-7.39

BALI7.9 NTT

5.2

KEP. RIAU7.8

LAMPUNG4.7

BENGKULU5.5

BANTEN8

SULSEL7.7

SULBAR10.9

JABAR5.5 JATIM

6NTB10.9

KALTENG5.3

KALSEL4

GORONTALO8.2

MALUKU3.5

SULTRA5.3

SUMSEL6

JAMBI9.6

DIY4.2

SUMBAR5.5

SUMATERA

I II III IV2014

5.454.82 4.37 4.37

234567

JAKARTA

I II III IV2014

6.00 6.06 6.02 6.22

234567

JAWA

I II III IV2014

5.21 5.28 5.436.02

234567

KALIMANTAN

I II III IV2014

2.43 2.513.58 4.00

234567

SULAMPUA BALI NUSRA

I II III IV2014

5.286.37 5.89

5.04

234567


On the other hand, the positive perception of investor on Indonesian economy leads to a significant capital inflow. The amount is sufficient to finance the ongoing current account deficit. On Quarter IV, 2014, the surplus of capital and financial account was supported by the foreign direct investment and surplus of other investment including the withdrawal of saving abroad and corporate’s foreign debt. However the surplus of capital and financial account is still lower compared to previous surplus on Quarter 3, 2014. The decline of the surplus is caused by the release of portfolio in Rupiah by foreign fund on December 2014 due to the plan of the FED to increase policy rate following the recovery of US economy.

2.3. Exchange Rate of Rupiah

The rate of Rupiah depreciates along with the appreciation of US dollar against many currencies. On Quarter IV, 2014, the Rupiah depreciate averagely by 3.9% (qtq) to the level of Rp12,244 per USD (Figure 9). The stronger US economy leads to an appreciation of US dollar against other currencies (Figure 10).

2.4. Inflation

Inflation is well maintained and move toward its target range of 4.0 + 1% in 2015. Inflation in Quarter 4, 2014 was 4.49% (qtq) or 8.38% (yoy), is higher than inflation in Quarter 3, 2014 of 1.68% (qtq) or 4.53% (yoy) (Figure 11). The price increase of the subsidized fuel and the larger volatility of food are the reason for the increase of inflation. However, the recorded inflation on Quarter IV, 2014 of 4.93% (yoy) is still in a maintained target, partly result of a good coordination between the government and Bank Indonesia.

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Figure 7.The Balance of Payment of Indonesia

Figure 8.Dynamics of Foreign Exchange Reserve


The inflation pressure from volatile food increased on Quarter IV, 2014 due to limited supply of Rice and Chili. Inflation on volatile food was recorded at 6.23% (qtq) or 10.88% (yoy), which is significantly higher than previous quarter of 2.11% (qtq) or 4.21% (yoy). The limited supply of Rice and Chili was caused by the draught started from the end of September to early November, and the heavy rainfall during the end of November to December. The cost push effect also contributed to the pressure of volatile food, due to the price hike of subsidized fuel on November 2014.

The inflation pressure from administered price commodities increased on Quarter IV, 2014, particularly due to the increase of subsidized fuel price. On Quarter IV 2014, the administered price inflation significantly jumped from 2.51% (qtq) or 6.53% (yoy) to 12.03% (qtq) or 17.57% (yoy). The government policy to increase the price of subsidized fuel on November 18, 2014 affect the price directly and indirectly (second round effect) via transportation cost. Furthermore, the increase of electricity price rate, household fuel, and air flight fare also contributed to the inflation pressure of administered price commodities.

The core inflation is well maintained during Quarter IV 2014, regardless the presence of the domestic and external pressure. The core inflation is 1.70% (qtq) or 4.93% (yoy), slightly higher than the previous quarter of 1.28% (qtq) or 4.04% (yoy). The external pressure arise from the weakening of Rupiah which is luckily in companion with the global price reduction as reflected on the lower imported inflation. On the other hand, the domestic pressure is originated from the cost push of fuel price increase and the slowing growth of economy.

The maintained core inflation on Quarter IV 2014 is in line with the expectation of inflation both in retail or consumer. On the retail level, the expected inflation due to the fuel price policy for 3 (three) and 6 (six) months ahead is lower than the episode of fuel price policy in 2013 (Figure 12). Besides that, the expected inflation of consumer for 3 and 6 months ahead also

Figure 9.The Rate of Rupiah

Figure 10.The Rate of Regional Currencies

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lower with the confidence that the effect of fuel price increase will be temporary (3 months started from November), and the optimism that the supply of goods will be better in next year.

Across region, the inflation pressure on Quarter IV 2014 generally increased. The inflation pressure was high and uniform across provinces in Indonesia during this quarter (Figure 2). The highest inflation pressure was in Sulawesi-Maluku-Papua (Sulampua), Bali-Nusa Tenggara, and Jakarta.

In January 2015, most provinces in Sumatera and Jawa recorded deflation while some of them recorded inflation. Deflation in Sumatera Barat occurred since the price of red chili was heavily corrected as well as the rice following the start of harvesting months. Within Jawa, the significant deflation occurs in Jakarta because the price of fuel lowered and the stock of Chili

Figure 11.Dynamics of Annual Inflation

Figure 12.Retail Price Expectation

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Inf>3.0% 1.0%<Inf<2.0% 0.5%<Inf<1% Inf<0%2.0%<Inf<3.0% 0%<Inf<0.5%

ACEH0.17

SUMUT-0.34

RIAU-0.61

KEP. BABEL1.1

DKI JAKARTA-0.41 JATENG

-0.35

SULTENG0.12

KALTIM0.97

KALBAR1.1

SULUT-0.71

MALUT-0.55

PAPBAR0.1

PAPUA0.15

BALI-0.17 NTT

0.61

KEP. RIAU-0.32

LAMPUNG4.7

BENGKULU-0.82

BANTEN-0.34

SULSEL-0.17

SULBAR0.14

JABAR-0.37 JATIM

0.2NTB0.48

KALTENG0.73

KALSEL0.18

GORONTALO-1.27

MALUKU1.7

SULTRA-0.61

SUMSEL-1.15

JAMBI-0.86

DIY0.13

SUMBAR-1.79

National Inflation : -0.24%

Picture 2.CPI Inflation Map across Regions (%, yoy)


increase significantly. Nevertheless, several provinces recorded higher inflation including Maluku, Nusa Tenggara Timur, and most of Kalimantan. The source of inflation on these regions is fresh fish, household fuel, and some food commodities such as chicken and vegetables.

III. THE DYNAMICS OF MONETARY, BANKING, AND PAYMENT SYSTEM

3.1. Monetary

The dynamics of interest rate and money supply is in line with the monetary policy of Bank Indonesia. During Quarter IV 2014, the interbank rate (PUAB) is relatively stable while the interest rate in banking industry is still in increasing trend. The increase of banking interest rate amidst the slowing economic growth on Quarter IV 2014 affected the national liquidity.

The interest rate on interbank money market (PUAB O/N) is stable with an increasing transaction volume. The weighted average interest rate PUAB O/N on Quarter IV 2014 is 5.81%, relatively stable compared to previous quarter 5.86% (Figure 13). The tight of liquidity in money market decrease as reflected in the lower overnight rate with longer maturity. This in turn reduced the PUAB spread between various maturities and the overnight one. Along with the lower liquidity pressure, the volume of interbank money market transaction increase by Rp12.9 trillion, higher than the previous quarter of Rp10.8 trillion.

The interest of the bank during Quarter IV 2014 is still increasing. The weighted rate of saving rate previously lower on the first two months this quarter due to the better liquidity and the implementation of OJK Cap, eventually increase on December 2014 in response to the hike of BI rate. Along with the increase of saving rate, the loan rate also increase with a lower magnitude and a lag. Quarterly, the loan rate increased by 8 bps from 12.87% to 12.95%.

The raise of weighted loan rate is mainly influenced by the increase of consumption credit by 20 bps to 13.58%. The rate for working capital loan (KMK) and investment credit (KI) correspondingly increased by 1 bps to 12.79% and 2 bps to 12.36%. With these dynamics, the spread between the lending and the saving rate (1 month) has narrowed to 437 bps from 439 bps, mainly because the increase of saving rate is larger than the lending rate (Figure 14).

Liquidity of the economy (M2) on Quarter IV 2014 increase mainly on quasy money. The growth of M2 has increased to 11.8% (yoy) from 11.7% (yoy). The increase of deposit interest rate and the slowing of economic activity lead the people to increase their saving on the banks. On the other hand, the M1 grow slowly from 9.4% (yoy) to 6.2% (yoy), driven by the growth of fiat money and time deposit (in Rupiah). They grow correspondingly to 9.8% (yoy) and 9.1% (yoy) from 4.9% (yoy) and 7.3% (yoy).

According to its determinant, the growth of M2 is mainly from an increase of net claim to the central government (NCG). On Quarter IV 2014, the NCG grow from 1.0% (yoy) to 2.5% (yoy), which is in line with the final government spending cycle. On the other hand, the net foreign active (NFA) slowed down.


3.2. Banking Industry

The financial stability remains solid with the viable banking system and the well maintained of the financial market performance. The viability of banking industry is strong with manageable credit risk and market liquidity. Furthermore, the strong capital also contributed to strengthen the banking industry.

The credit growth slowed down particularly for working capital (KMK). The credit growth on Quarter IV 2014 again decreased to 11.6% (yoy) from previous quarter of 13.2% (yoy) (Table 2). The slower growth of total credit is largely affected by the contraction of KMK from 13.3% (yoy) in Quarter III to 10.8% (yoy) in Quarter IV 2014. Furthermore, the growth of investment credit also decreased from 16.4% (yoy) to 13.2% (yoy) on Quarter IV 2014. In contrast to KMK and KI, the consumption credit (KK) recorded a positive growth from 10.1% (yoy) to 11.5% (yoy). Across sectors, the contraction of the credit occurred on Trade, and Manufacturing, correspondingly from 13.9% (yoy) and 16.1% (yoy) to 12.4% (yoy) and 14.3% (yoy).

The growth of third party fund (DPK) on Quarter IV 2014 also decreased. It recorded a growth of 12.3% (yoy), lower than previous quarter of 13.3% (yoy). The slowing down of deposit occurred across all components (Figure 15).

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Figure 13.Interest Rate Corridor of Monetary Operation

Figure 14.Total Volume of Interbank Transaction

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Amidst the slowing growth of the loan, the banking industry still performs well. On Quarter IV 2014, the amount of capital is still sufficient with the recorded Capital Adequacy Ratio (CAR) of 19.36%. Meanwhile, the profitability of the bank as reflected on the Return of Asset (ROA) remains high of around 2.85% (Table 3).

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Figure 15.The Growth of Third Party Fund


3.3. The Stock and Sovereign Market

The dynamics of domestic stock market during Quarter IV 2014 recorded positive performance with the support of positive domestic sentiment. The composite stock market index (IHSG) recorded 5,226.95 (December 31, 2014), which increase by 1.7% (qtq) relative to Quarter III, 2014 of about 5,137.60 (Figure 16).

The stronger capital market is partly due to positive sentiment of investors on the successful government transition. Furthermore, the government policy to increase the fuel (November 17, 2014) followed by the increase of BI rate by 25 bps were also gain positive response from market participants. However, the anticipation of global investor on the FOMC result on mid of August 2014 was hawkish and created pressure on the capital market.

The pressure on capital market decreased after the FOMC state that the Fed will postpone to increase its rate. Relative to other countries within the region (Vietnam, Philippines, Thailand, and Malaysia), the stock market in Indonesia during Quarter IV 2014 still performed well. Overall, the stock market on the region recorded negative growth while Indonesia stock market recorded positive growth.

The sovereign market performed better supported by positive domestic sentiment. As previously stated, the smooth government transition, the raise of BI rate to anticipate inflation after the fuel price increase were positively responded by the market and strengthen the sovereign stock market. The global investors re-enter the domestic bond market and lead the yield of government bond to decline by the end of 2014. Overall, the yield declined by 57 bps to 7.89% from 8.37% in previous quarter (Figure 17). The short term, the medium, and the long term yield correspondingly decline by 35 bps, 65 bps, and 70 bps to 7.41%, 7.78%, and 8.36%.

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The positive of sovereign market lead to net buy position by foreign investors. During Quarter IV 2014, the non-resident keep buying even though the volume of transaction is lower compared to Quarter III, 2014. The net buy of non-resident was Rp13.99 trillion, which is significantly lower than previous quarter, Rp43.79 trillion. In December 2014, the net sale was Rp19.84 trillion in total following the negative external sentiment. The yield in Indonesia is relatively higher than several countries within the regions due to pressure and the high inflation on December 2014.

During Quarter IV 2014, the foreign ownership on sovereign bond increased. On this quarter, foreign investors own 37.04% of the total sovereign bond, and are higher than the previous quarter. The buying action occurred on most maturity with the highest was on the longest maturity (Figure 1.47). Relative to previous quarter, the ownership of sovereign bond by the bank and insurance company decrease to 28.45% and 12.09% correspondingly. The sovereign bond owned by the pension fund is stable, while by Bank Indonesia increased.

3.4. The Payment System

The development of the payment system with cash is in line with the development of domestic economy, particularly with the domestic household consumption. On average, the distributed fiat money (UYD) was Rp478.6 trillion on Quarter IV, 2014 or grew by 6.8% (yoy). The growth is lower compare to Quarter III 2014 of 12.6% (yoy).

Amidst the slowing growth of the distributed money, Bank Indonesia continued to ensure the feasibility to use of this money. During Quarter IV 2014, there is 1.5 milliard pieces of non-usable money (UTLE), worth Rp30.7 trillion. They are destroyed and replaced with a good and

Figure 16.Composite Stock Market Index and Global Stock

Index, Quarter IV 2014 (qtq)

Figure 17.The Dynamics of Yield , Quarter IV 2014

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feasible to use money. This amount is higher compared to previous quarter where 1.3 milliard pieces of money, worth Rp29.7 trillion were recalled and destroyed.

Within the payment system, transaction runs safely and smoothly during Quarter IV 2014. Relative to previous quarter, the transaction is higher both in volume and values. The transaction was recorded at Rp4,526.9 trillion or increase by 11.0% (qtq) (Table 4), while the value of transaction increased to 63.1 million transactions or grew by 5.3% (qtq). In general, the increase of transaction value occurred in most transaction group, particularly on monetary operation with a growth of 15.0%, equivalent with Rp2,483.0 trillion. On the other hand, the transaction by the market participant via non cash instrument particularly provided by the industry also contributed to the increase of transaction volume. The highest transaction volume used card (APMK) which grow by 3.9% (qtq) or 43.6 million transactions.

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Along with the increase of the value and the volume of transaction during Quarter IV 2014, the payment transaction settled via BI RTGS system also increased. The availability of the Real Time Gross Settlement (BI-RTGS) to settle fund, the BI-SSSS to settle the government and central bank securities, and also SKNBI, reached 100 percent during Quarter III 2014.

The value of payment transaction through BI-RTGS system increase by Rp3,169.3 trillion (grew by 10.6%, qtq), from Rp29,872.4 trillion in previous quarter to Rp33,041.6 trillion. On the other hand, the volume of transactions increased by 60 thousands (grew by 1.3%, qtq); from 4.52 million transactions in last quarter to 4.58 transaction in Quarter IV 2014.


IV. ECONOMIC OUTLOOK

Bank Indonesia predict the economic growth on next year 2015 will be higher than 2014 of about 5.4 – 5.8 percent. The increase of growth mainly due to the strong household consumption and the positive contribution of the fiscal capacity expansion to support the productive sectors, including infrastructure projects. The plan is outline on APBNP 2015 which is already granted by the legislatives. Moreover, the development in investment climate is expected to further increase the investment,including the one gate system (PTSP). The contribution of export on economic growth is also expected to increase even though with relatively insignificant magnitude.

The inflation on 2015 is expected to be lower than 2014 and lie on lower zone of the inflation target. The well maintained core inflation, the lower crude oil, and better food supply will contribute to the lower inflation. The well maintained expected inflation, lower commodity prices, and the low pressure of demand help to maintain the core inflation.

The lower crude oil price and the better supply of food commodities will help to control the administered inflation and the volatile food inflation. With these figures, by the end of 2015 we expect to have inflation of around 4 + 1 %. On the other hand, the coordination between Bank Indonesia and the government will set the inflation by the end of 2016 of also around 4 + 1%.

Bank Indonesia will keep controlling and regulating the domestic economic risk, which have been minimized with several structural reforms taken by Bank Indonesia and the government. On the global side, the plan of the Fed to normalize his policy will be partly counter balance with the plan of monetary stimulus in Europe. A better and higher quality of government fiscal structure will also help to increase the foreign capital to flow in. On the other hand the structural reform is expected to also increase the export competitiveness and restrain the negative impact of China’s slowing economy. On the external side, the pressure on inflation will be minimal with the commodity price decline, the fuel prices decline, the lower demand, and with the food sustainable policy of the government.



289The Role of Currency Hedging on Firm Performance: A Panel Data Evidence in Indonesia

The Role of CuRRenCyhedging on fiRm PeRfoRmanCe:

a Panel daTa evidenCe in indonesia

Fiskara IndawanSri Fitriani

Indriani KarlinaMelva Viva Grace1

This paper analyzes the role of currency hedging on non-financial firm’s performance. Most firms on

the sample have anticipated the currency mismatch risk by balancing the ratio of foreign debt to their asset

denominated in foreign currency. Using panel estimation, we find that there is no evidence of currency

hedging activities to affect capital and performance of firms. The result underlines the low intensity of

currency hedging activities due to lack of incentives, which is inline with the low derivative transaction

within the underdeveloped foreign currency market. This finding may raise a concern since currently the

development of foreign liabilities for non-financial firms in Indonesia is increasing in significant level, as

well as the increase risk of domestic currency depreciation. For these reasons, Bank Indonesia should take

proactive policies to deepen foreign currency market as well as derivative market by providing a more

comprehensive and market friendly hedging instruments to banks and non-financial firms, while keep

promoting the benefit of currency hedging.

abstract

Keywords: Hedging, derivative market, foreign liability.

JEL Classification: F31, G31

1 Researcher on Economic Research Bureau, Department of Economic and Monetary Policy Research, Bank Indonesia. The views on this paper are solely of the authors and do not represent the views of Bank Indonesia. Authors thanks to Dr. Iskandar Simorangkir and Dr. Noer Azam for constructive input and discussion. E-mail: [email protected] (corresponding author),[email protected], [email protected], [email protected].


I. INTRODUCTION

In the midst of global economy, conditions are still not showing certainty at present. The total foreign debt,which has been increasing since 2006, can give rise to adverse effects (upside risk) on the economy. At the end of September 2012, the total foreign debt stood at USD 243.9 billion. Of the total foreign debt, the share from non-financial companies was 35% or USD 85 billion (for the average period March 2003 - September 2012) (Figure 1). The total foreign debt is quite large, especially that owned by the non-financial corporate sector that is exposed to the potential risk of foreign exchange incompatibility (currency mismatch) as a result of fluctuations in exchange rate movements, particularly in the case of exchange rate depreciation.

The rupiah exchange rate against the USD is highly volatile as a result of domestic and external developments (Figure 2). For example, the exchange rate depreciated sharply in 1997 with the Asian financial crisis as a result of a contagion effect to the crisis in Thailand and the loss of foreign investor confidence in the Indonesian domestic economy. In 2005 there was a mini-crisis as a result of a surge in inflation, which reached 17% due to higher fuel prices; and the last major fluctuation was at the end of the 2008 global financial crisis. Looking ahead, the global economy is still showing uncertainty amidst the potential for increased exchange rate fluctuations, notably depreciation.

Figure 1.Total foreign debt movement and Non-Financial

Companies

Figure 2.Exchange Rate Fluctuations against USD

Miliar USD

250 30%

25%

20%

15%

10%

5%

0%

-5%

-10%

-15%

200

150

100

50

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Mar Sep Mar Sep Mar SepMar Sep Mar Sep Mar SepMar SepMar SepMar Sep Mar Sep0

Source: CEIC

Total Foreign Debt non-financial companiesTotal Foreign DebtTotal Foreign Debt Non- FinancialCompanies

Source: CEIC

JanJul

1997

JanJul

1998

JanJul

1999

JanJul

2000

JanJul

2001

JanJul

2002

JanJul

2003

JanJul

2004

JanJul

2005

JanJul

2006

JanJul

2007

JanJul

2008

JanJul

2009

JanJul

2010

JanJul

2011

JanJul

2012

-

16,000.00

14,000.00

12,000.00

10,000.00

8,000.00

6,000.00

2,4536.75

6,805.97

4,000.00

2,000.00

9,361.90

13,876.14

11,291.19

8,433.71

9,986.18

11,852.75

8,555.80

For companies, financing through foreign debt (external debt, or ED) can be likened to a double-edged sword. On the one hand, the ED provides financing for companies both for production and business expansion. But on the other hand, a high ED would result in exposing companies to the risk of exchange rate incompatibility (currency mismatch risk). Theoretically,


exchange rate depreciation has two effects on the economy, i.e., the balance-sheet effect, especially when reviwing a company’s condition, and the competitiveness effect. The balance-sheet effect occurs when the burden of the foreign debt currency is great, which would then reduce the capital (net worth) of companies and likely reduce the ability of the company’s investment and output. The competitiveness effect occurs when the exchange rate depreciation leads to modest prices of export goods in the country, there by increasing the volume of exports of the company and the domestic output.

The economic and monetary crisis in 1997 was a bad experience of sharp exchange rate depreciation that resulted in soaring burdens of installments and principal payments for foreign debt companies. Companies experienced difficulties in foreign debt and payment default,as much of the revenue earned was in domestic currency. In anticipation of potential exchange rate risk (currency mismatch) as a result of the sharp depreciation of the exchange rate, firms hedged against the foreign debt in order to protect aganist bankruptcy. One way of hedging utilized by companies was to use derivative transactions consisting of forward, swap and options (currency hedging) transactions.

However, the volume of derivative transactions in the foreign exchange market of Indonesia is still limited, especially when compared with regional countries (Figure 3). This has resulted in limited development of derivative transactions that are part of the transactions in the domestic foreign exchange market (Figure 3). During the period 2005 - 2011, most of the foreign exchange transactions carried out was in the form of Today, Tomorrow, and Spot (TTS), representing 68% of the total volume of transactions, and the rest of derivative transactions was in the form of a swap, forward and option. Besides this, the role of non-financial companies in the foreign exchange and the derivatives markets has not shown satisfactory progress.

Figure 3.Daily Volume of Foreign Exchange Trading in the

ASEAN Region

Figure 4.The Development of the Foreign Exchange Market

Instrument (Total Volume)

USD Miliar

Source: LHBU – BI

8

7

6

5

4

3

2

1

01998 2001 2004 2007 2010

IndonesiaMalaysia

FilipinaThailand

Miliar USD

260240220200180160140120100

80604020

0

2005

/Aug

2006

/Jan

2006

/Jun

2006

/Nov

2007

/Apr

2007

/Sep

2008

/Feb

2008

/Jul

2008

/Des

2009

/May

2009

/Okt

2010

/Mar

2010

/Aug

2011

/Jan

2011

/Jun

2011

/Nov

2012

/Apr

TTSSwap

OptionTotal

Forward

Source: BIS Survey 2010


The largest market players in the foreign exchange and derivatives markets in Indonesia is still dominated by domestic banks that control almost 64% of total transactions in the foreign exchange market and 70% in the derivatives market (Figure 5 and 6). This contrasts with the non-financial companies where there role is small at about 12% in the foreign exchange market and 7% in the derivatives market.

There are three types of instruments in the domestic derivatives market. The first is the swap, which is the largest instrument in the derivatives market that is dominated by the banking agents (74%), followed by foreign parties(22.70%), companies (2.80%), and individuals (0.27%) (Figure 5). The second instrument, forward transactions (Figure 6), arealso dominated by banks (51.84%), followed by the companies (31.42%). The third instrument, option transactions (Figure 7), is a very small relative to the other instruments where only USD 389.8 million comprises the role of individuals and is the majority (14.5%), greater than the transactions made by companies (9.3%).

Figure 5.Share Performance of Swap Transactions(%)

Figure 6.Share Performance of Forward Transactions(%)

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

2005

/Sep

2006

/Jan

2006

/Mei

2006

/Sep

2007

/Jan

2007

/Mei

2007

/Sep

2008

/Sep

2008

/Jan

2008

/Mei

2009

/Sep

2009

/Jan

2009

/Mei

2010

/Sep

2010

/Jan

2010

/Mei

2011

/Sep

2011

/Jan

2011

/Mei

2012

/Sep

2012

/Jan

2012

/Mei

Source: LHBU – BI

Domestic-BankDomestic-Individuals

Domestic-CompaniesForeign

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

2005

/Sep

2006

/Jan

2006

/May

2006

/Sep

2007

/Jan

2007

/May

2007

/Sep

2008

/Sep

2008

/Jan

2008

/May

2009

/Sep

2009

/Jan

2009

/May

2010

/Sep

2010

/Jan

2010

/May

2011

/Sep

2011

/Jan

2011

/May

2012

/Sep

2012

/Jan

2012

/May

Source: LHBU – BI

Domestic-BankDomestic-individuals

Domestic-CompaniesForeign

Figure 7.Share Performance of Option Transactions (%)

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The fact that the volume of the swap and forward transaction instruments continue to increase (Figure 8 and 9), while the option transactions have seen sharp decline since June 2009 (Figure 10), is a positive indication of market expectations about the economy. The logic is obvious given the option instruments are classified as insurance instruments.

Apart from the positive indications of the market expectations, the trend shows that the value of the Rupiah tended to depreciate until early 2015 (Figure11). Looking ahead, it is a still fragile recovery process, with the high uncertainty of the global economy, and the risk of imbalances in the balance of payments which could potentially increase a capital flow reversal at

Figure 8.Swap Transaction (Volume Total)

Figure 9.Forward Transactions (Volume Total)

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Figure 10.Option Transactions (Volume Total)

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any timeand increase the vulnerability risk for further depreciation of the rupiah. In consideration of this, a company that has foreign currency is required hedge transactions (hedging) in order to manage exchange rate risks and to maintainthe company’s financial condition.

This paper investigates the role of enterprises in currency hedging for managing foreign exchange risk. Explicitly, the purpose of this paper is to analyze the effect of hedging activities on corporate performance. The second section of this paper outlines the theoretical and empirical studies related to the subject. The third section presents the data and methodology used, while the fourth section outlines the results and discussion. Conclusions are given in the fifth and closing section of this paper.

II. THEORY

The effect of exchange rate fluctuations on the financial condition of companies that have a foreign currency debt can be explained through the concept of balance-sheet effect. Several authors explain the concept of the balance sheet effect such as Krugman (1999), Cespedes (2000) and Aghion (2001 and 2003). In general, the concept holds that the exchange rate fluctuations will affect a company’s ability to expand and produce. Through the balance-sheet approach the impact of the exchange rate depreciation against an economic recession can be explained. Foreign debt burden would rise rapidly due to the depreciation which would reduce a company’s capital (net worth) resulting inreduced capability for production and investment. Many companies that went bankrupt in the 1997 Asian financial crisis pushed the domestic economy into recession.

According to Aghion et al. (2001), the interaction between exchange rate fluctuations with the firm’s output can be explained in terms of entrepreneurial wealth. A company will rely on the capital of the entrepreneur(s) as an input in the production process as shown by the following equation :

Figure 11.Exchange Rate against US Dollar

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(1)

(2)

(3)

Furthermore, to add to the wealth required for the production process in the next period, the entrepreneur borrows funds from domestic and abroad to pay interest and , respectively, at the nominal exchange rate . Therfore, the entrepreneur profit function equation is:

Entrepreneurs will then use the profit earned in the current period as wealth which in the next period would be:

This wealth will be used as inputs of production for the next period in order to obtain:

is the real interest rate as defined as .

Equation (4) shows that the output produced by the company will go down if there are cost overruns in foreign debt resulting from the depreciation of the exchange rate. Companies with foreign debt would see capital decreases with depreciation. In order to maintain the financial condition through wealth, companies would be required to hedge against their foreign debt. Or in other words, the company will attempt to get currency matching their foreign debt in order to maintain the balance / equality of conditions of its balance sheet.

Companies that have ED can do a number of things to manage its exchange rate risks. According to Bodie and Merton (2000), first, avoid risk by not making any transactions that could have an impact on the exchange rate risk. Second, reduce the risk of loss, with for example, appropriate site selectionfor production, especially for multinational companies. Third, transfer the risk to another party through the use of hedging, derivative instruments, insurance, and diversifying risks by using several different suppliers. Fourth, resist risks (by not doing anything) as long as the risk is still at a rational level.

Various studies have been done to determine the role of currency matching and currency hedging for integration in the context of foreign exchange risk management in some countries. Other research has found significant results regarding the role of currency matching and currency hedging in foreign exchange risk management, particularly with the risk of currency mismatch. Kamil, et al. (2009), using a sample of 1,200 public companies in Latin America, found that in the last decade, companieshad reduced vulnerability to exchange rate risk by reducing currency mismatch on its balance sheet. This was done by reducing the reliance on foreign currency debt

(4)


and then matching liabilities with assets in foreign currency or with a foreign currency flow expectations. Meanwhile, Cowan etal., (2005) conducted a study on non-financial companies in Chile using panel data. The empirical results of their study showed that: (1) companiesdid currency matching between currency debt by assets or income; (2) companies actively hedged against exposure balance (balance sheet effect); (3) the use of derivatives played a role in protecting company investments from exchange rate shocks.

Empirical studies on foreign exchange risk management done by Sahminan (2006) in Indonesia used industry-level data of the non-financial sector listed on the Indonesia Stock Exchange (IDX), that included sectors such as agriculture, mining, consumer goods, basic industry and processing industry. An anecdotal survey was also conducted,and found that the hedging strategy of companies varied, as some companies used hedging (activities), while others did not. The hedging instruments used included, forward, swap and option. The main reason some companies did not hedge was because hedging costs were greater than the benefits.

Further studies on the use of foreign currency debt as a hedging instrument was carried out by Kedia and Mozumdar (2003) by using probit. There was strong evidence that hedging is a company’s main motivation to borrow using foreign currency debt, i.e. to protect the company from exchange rate exposure at the aggregate level as well as individual companies. Borsum and Odegaard (2004) examined the use of derivative instruments by non-financial companies in Norway for the managing the exchange rate. Using a survey method, it was found that: (1) non-financial companies commonly used derivatives as instruments for foreign exchange risk management, (2) the use of derivatives is still largely directed at short-term, (3) the reason companies do not hedging is related to costs - hedging costs were perceived to be greater than the benefits.

Another study conducted in East Asia (Allayanis, et al., 2001) analyzed the management of the exchange rate at the corporate level. The empirical results showed that companies use foreign exchange earnings as a substitute for hedging and selectively uses derivative instruments.Among the companies, there were no differences in performance both for companies that use derivatives and those that did not, at the period before the crisis and during the crisis. But after the crisis, companies that used derivatives had better performance.

On the other hand, the depth of the derivatives market was another factor that was seen essential in the development of the foreign exchange market, especially for countries like Indonesia that apply exchange rate policy such as a floating exchange rate. Foreign exchange transactions are transactions based on a contract of sale and purchase of currency with other currencies. Foreign exchange transactions can function as, (1) a hedge (hedging) that is transferred to a counterparty risk of the underlying holder, or (2) speculation that is usually done without underlying profit. Types of foreign exchange transactions consist of;Today transaction (TOD), Tomorrow (TOM), SPOT and derivative transactions. Derivative transactions are transactions based on a contract or agreement whose value is derived from the value of


underlying instruments such as interest rates, exchange rates, commodities, equities and indices, followed either by movement or without movement of funds or instruments. Instruments include derivative transactions in foreign currency forwards, options, swaps, and futures.

The foreign exchange derivatives market is developing and is expected to contribute to the availability of the instrument of choice to manage the transaction exchange rate risk (currency risk management) for both companies and households. The availability of the hedging instrument is expected to help balance supply and demand in the foreign exchange market, so as to reduce the exchange rate volatility.

Experience in emerging markets showed mixed result regarding the relationship between derivative trading activity on the foreign exchange market with exchange rate volatility. Research conducted by the Central Bank of India showed that there is a two-way causal relationship between exchange rate volatility (spot) with trading activity in the form of futures derivative transactions in the foreign exchange market (Sharma, 2011). Chen, etal. (2011) also found that the behavior of investors in the futures market in South Korea showed a significant positive relationship between the volatility of the exchange rate Won / USD and the volume of futures transactions conducted by both banks and foreign actors. Causality in one direction occurred between the volume of transactions conducted by both banks and foreign players to the exchange rate volatility in the futures market of South Korea..

Sharma (2011), and Chatrath, Ramchander and Song (1995) showed evidence of a strong one-way relationship between, futures trading activity in the foreign exchange market with an exchange rate volatility spot rate for the British pound, Canadian dollar, Japanese yen, Swiss franc and Deutsche mark. Meanwhile, Adrangi and Chatrath (1998) and Sharma (2011) concluded that the growth in currency futures commitment does not result in increased volatility in the exchange rate, but an increase in bulk and limited speculation by traders / hedgers that contributes to volatility in the foreign exchange market.

III. METHODOLOGY

1.1. Empirical Model

To determine the effectiveness of hedging transactions, we construct empirical model to test the impact of hedging transactions volume on the net wealth of companies and on the companies’ performance. The test was carried out using two approaches; the first was the company’s balance sheet (balance-sheet effect) following an empirical model of Kurniati et al. (2007), and the measurement of the company’s performance following the empirical model of Allayanis et al. (2001).

In the first approach, we modify the control variable previously estimated on Kurniati et al. (2007). In this model, the company’s net worth is the proxy for the company’s capital. The


In this case, is the net worth of the company; is the net foreign assets (foreign assets - foreign liabilities); is the exchange rate;

is the interest rate working capital; is domestic debt; and is the derivative transaction.

The main variables in this model are , that is the interaction between the net foreign assets (NFA, the difference between foreign currency assets with foreign currency debt) by changes in the exchange rate. The coefficient of this variable indicates the effect of which is more dominant, either the balance-sheet effect or the competitiveness effect. A positive coefficient indicates the effect of increasing the competitiveness of the more dominant, while a negative coefficient indicates a deteriorate effect on the company balance sheet as more dominant. To capture the effects of NFA regardless of the magnitude of changes in the exchange rate, isused,while the variable is used to see how big the effect of the exchange rate changes regardless of the NFA. Furthermore, the control variables used are in debt with the domestic rate to measure the level of leverage, interest rates on working capital, and the volume of derivative transactions undertaken by a company (DERT).

In addition to the effectiveness of hedging transactions on net worth, tests were also conducted on the company’s performance as used by Allayanis et al (2001) with the following specifications:

In this case, is the ratio of the total derivative of the assets of the company; = ratio foreign asset against a company’s assets; = ratio foreign liabilities

againts the company’s assets; and = export ratio of company sales.

The volume of hedging transactions is an independent variable. Some indicators of corporate performance are used as a dependent variable, like net profit and the company’s cost of goods sold. Another indicator is the test for the use of market-to-book ratio and EBIT as used by Allayanis et al. (2001).

(5)

(7)

(6)

EQ

model is derived from the simplified corporate balance sheets, in order to obtain the reduced form as follows:

If then:

EQ


1.2. Data

The main data of this study are the annual financial statements of 128 companies listed on the Indonesia Stock Exchange during the period 2005 - 2011. The data includes total assets, liabilities, equity, foreign assets and foreign liabilities. Other detailed data and sources include foreign exchange transactions, export and import, and external debt and the exchange rate is as shown in Table 1.

As mentioned earlier, derivative transactions in this paper uses data obtained from the Commercial Bank Daily Report (CBDR). In the CBDR, the volume of derivative transactions is part of the foreign exchange transactions in the domestic market and consists of three instruments, namely forward, option and swap. The data volume of the derivative transactions for each transaction are grouped into the volume of transactions to buy, sell, and total.

IV. RESULT AND ANALYSIS

The estimation results indicate that the use of derivative instruments have not been proven to significantly affect the net worth of companies (Table 2 and Table 3). However, a company’s net worth is significantly and negatively influenced by the interaction between the foreign assets of the exchange rate and the exchange rate lag. The NFA lag and DL (Domestic Liabities) lag affects the net worth significantly and positively. These findings indicate that companies are still vulnerable to exchange rate exposure, even though they tried currency matching. The following description will show that the efforts made by these companies, were not comparable with the potential currency mismatch they faced.

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The test results with the two approaches showed that hedging transactions still have no affect on the net worth (company capital) and the company’s performance significantly. This indicates that there has not been a strong incentive for companies to conduct currency hedging in order to protect the value of wealth and performance of the company against exchange rate risk exposure. Further discussion below outlines why companies have not made hedging transactions to protect their value of wealth and corporate performance.

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Although the companies in the sample of this research had been trying to balance between the ratio of foreign liabilities and the ratio of foreign assets, there was still the potential risk of exposure to currency mismatchs (Figure 12). The decline in the ratio of foreign liability occurred quite rapidly from 53.2% (2000) to 22.6% (2004). However, the gap between the ratio of foreign liabilities to foreign asset ratio remained relatively wide. In 2011, the ratio of foreign liabilities was 17%, while the ratio of foreign assets was 6.3%. Although still relatively wide, the gap illustrates that companies continued facing the risk of currency mismatch.

The estimation results showed that there was no significant effect of hedging on the net worth of companies. The above data showed that although companies experienced a high risk of currency mismatch, only a small number of the sample companies conducted hedging through derivative transactions. After reaching a peak in 2008, there was a decline in companies that used hedging transactions (44 companies or 34% of the total sample) (Figures 14 and 15).

In 2011 only 20 companies (16%) entered into derivative transactions. The high corporate conduct of hedging transactions in 2008 was a result of the global crisis that led to the increased expectations of exchange rate risk. However, since mid-2009, with the recovery in the domestic economy and the recovery of the Asia Pacific region from the impact of the global crisis, there were decreased expectations of exchange rate risk, which also saw a decline in derivative transactions on the sample of companies. When viewed from the type of instruments used, the derivative transaction widely used were the forward and swap transactions, while the use of option transactions was relatively small. In 2010 and 2011 none of the companies samples used option transactions.

Figure 12.Developments of Total Assets, Liability and

Foreign Assets

Figure 13.Development of Foreign Liability Ratio and

Foreign Asset Ratio

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Figure 14.Number of Businesses Conducting Hedging

Figure 15.Percentage of Companies Conducting Hedging

against Total Sample

The results of testing of the effectiveness of hedging on corporate performance shows that the ratio of derivative transactions empirically did not significantly affect the company’s performance including the ratio of net income, EBIT ratio, and the ratio of operating expenditures. General corporate performance indicators were significantly affected by the ratio of foreign assets and liabilities.

Based on financial statements published by the 128 non-financial companies that had gone public, there was a trend of an increase in foreign liabilities and foreign assets, followed by total assets (Figure 16). Except in the 2000-2004 period, the development of companies with foreign liability remained high and increased, reaching Rp 133 trillion in 2011. This upward trend was also experienced in the foreign assets of companies which reached Rp49.5 trillion in 2011. Since 2004, in nominal terms, the total assets of companies continued to increase, reaching Rp 800 trillion by the end of 2011.

Efforts were made to explain the inability of the hedging transactions undertaken by companies to affect their net worth as well as their performance, in terms company export and import activities. For a majority of the sample companies (approximately 60% of the total sample or 77 companies) saw export activities as a source of foreign exchange receipts (Table 4 and Table 5). Most of the companies (55 companies) that did export, also had expenses in foreign currency in the form of imports.

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However, among the companies that exported, only 23 companies or 18% of the total sample,hedged transactions. In addition, the companies that did not export, increased import activities (Table 6). Of these companies, only 11 companies (or 9% of the total sample)used derivative transactions. Both of examples indicate an increased risk of currency mismatch for these companies.

In currency matching, companies tend to rely more on other sources of foreign exchange earnings, among other foreign currency deposits. In addition, a factor for determining why a company did not hedge was related to an increase in the ratio of foreign currency debt. Another factor was the high cost of hedging derivatives transactions.

To ascertain this, further research was conducted on the companies’ behavior in the hedging, based on the results of the Risk Management Survey conducted by the Directorate of Bank International Indonesia. Based on this survey, one of the reasons a company did not hedge through derivative transactions was due to the cost to conduct such transactions as well as the perception of domestic economic instability ahead. This was also reflected in the total assets of the sample companies, where most of the companies that entered into derivative transactions (43 companies) had total assets of over Rp 1 trillion (Figure 16). This showed that companies that conducted hedging transactions were big companies.

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Figure 16.Total Assets of Businesses Doing Derivative

Transactions

The survey was conducted by the International Department of the Bank in June 2011, involving 117 company owners with money abroad. This survey showed that the share of companies that conducted derivative transactions for hedging was limited (only 21% of the sample companies, Figure 17). While the main instruments used for hedging were swap and forward transactions.

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Figure 18.Shares (%) of Instrumentsused by Companies

Figure17.Forex trading company (Volume Total)

Forthe non-financial companies, the volume of transactions conducted constantly increased (Figures 5, 6 and 7). Just like other market participants, non-financial companies do more foreign exchange transactions in the form of TTS that was equal to 81% of total transactions. The derivative transactions carried out by non-financial companies were more in the form of forward transactions (10%), followed by the swap transactions (6%) and option transactions (3%).

The survey also examined the companies that did not hedge, namely: (1) companies that wholly / partly exported production or sold domesticly using standard currency (natural hedge), (2) companies with a perspective of a relatively stable global and domestic economy so that the perceived risk of exchange rate was relatively low, (3) companies with the perception of hedging costs as relatively expensive compared to the benefits obtained, and (4) companies with a subsidiary company with foreign exchange earnings.

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Figure 19. Percentage of Sample Companies that conducted Hedging

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From the survey,it was found that the efforts of companies with external debt (that do not own currency receipts and do not hedge), (1) search for sources of foreign currency funds that enter into the market gradually by taking into account the economic conditions such as a favorable exchange rate and the company’s finances (30% of the sample); (2) search for sources of foreign currency funds into the market at the time they made external debt payments (21% of the sample); (3) borrow foreign currency funds from the parent company / affiliation (19% of the sample); (4) use of foreign currency savings of the company (12% of the sample); and (5) do a debt to equity swap or seek diversified sources of revenues of other foreign currency, or not take action because the condition is not yet stable (12% of the sample).

From the estimation results, the description data, and the above survey showed no significant use of hedging activities on company performance. Companies utilized other efforts than hedging to mitigate exchange rate risks as part of an effort of currency matching that includes borrowing from the parent company or by doing a debt-to-equity swap.

V. CONCLUSION

This paper examined the extent to which the role of hedging, through derivative transactions (currency hedging) of non-financial companies that have gone public, to hedge externally-owned debt as an approach, balance sheet effect. The results of empirical testing using the panel data method for non-financial companies from 2005 - 2011 confirmed that the role of currency hedging to hedge liability on the external debt was limited. Currency hedging does not have a significant role in a company’s performance as indicated by the low incentive by companies to undertake currency hedging. A company utilizes other efforts than hedging to mitigate exchange rate risk. Such efforts would include taking a loan from the parent company or do a debt-to-equity swap.

Using equity data and the mapping in the foreign exchange market showed that the derivatives market in Indonesia is still not well developed in terms of using this instrument, as well as the market participants. The domestic foreign exchange market is still dominated by cash transactions in the form of TTS, where derivative transactions only contribute about 30% of total transactions. In addition,a company’s role in derivative transactions is relatively small at approximately 7% of the total transactions in the foreign exchange market. Both TTS and derivative transactions are still dominated by the banking sector.

From the Survey of Risk Management that was done by the International Department of the Bank of Indonesia, a company did not hedge as a result of several factors -- the company did natural hedging, had a perception of low risk on the exchange rate, the relatively high cost for hedging, and the company owned subsidiary income in foreign currency. Companies that did not hedge had some way to meet foreign obligations such as seeking sources of foreign currency that entered the market gradually by taking into account the economic conditions and a favorable exchange rate, and taking loans from the parent company or its affiliates.


Based on the above test results, and given the potential risk of a sharp depreciation of the exchange rate can occur unexpectedly, Bank Indonesia needs to continue to develop policies and regulations that aim to deepen the foreign exchange market and the domestic derivatives market (market deepening). Some of these policies should include provisions for currency hedging instruments that are more comprehensive and market friendly to banks and companies. Identification of the types of instruments appropriate for hedging can be communicated to large companies that have a foreign currency debt and its leading bank. In addition, the next policy issued by Bank Indonesia and its related parties needs to be disseminated to companies who have external debt, to educate them on the benefits of currency hedging transactions to protect their financial condition in case of unexpected depreciation. Socialization can be done in collaboration with the Government through the relevant ministries and the private sector through related associations.


RefeRenCes

Aghion, Phillipe, Philippe Bacchetta and Abhijit Banerjee(2001). Currency Crises and Monetary Policy in an Economy with Credit Constraint. European Economic Review 45 (2001) 1121–1150

Allayanis, G, Gregory W. Brown and Leora F. Klaper (2001). Exchange Rate Risk Management: Evidence From East Asia. Working Paper No. 01-09, Darden Graduate School of Business Administration, University of Virginia

Borsum, Øystein G. and Bernt Arne Ødegaard (2005). CurrencyHedging in Norwegian Non-Financial Firms. Economic Bulletin 05 Q3

Cespedes, Luis Felipe, Roberto Chang and Andrés Velasco (2004). Balance Sheets and Exchange Rate Policy. The American Economic Review, Vol. 94, No. 4, pp. 1183-1193. American Economic Association

Chen, Dar-Hasin, Hwey-Yun Yau, Chin-Lin Chuang, and Po-Cheng Kuo (2011). Trading Behaviors Among Major Investors in The United States Dollar (USD) Currency Futures Markets: Evidence From South Korea. African Journal of Business Management, Vol.5 (28), pp. 11295-11308

Cowan, Kevin, Erwin Hansen, and Luis Oscar Herrera (2005). Currency Mismatches, Balance-Sheet Effects and Hedging in Chilean Non-Financial Corporations.Working Paper #521, Inter-American Development Bank, Research Department, Departamento de Investigación, Central Bank of Chile

Kamil, Herman, (2009). How Do Exchange Rate Regimes Affect Firms’ Incentives to Hedge Currency Risk inEmerging Markets?, International Monetary Fund, Washington DC.

Kedia, Simi, Abon Mozumdar (2003). Foreign Currency–Denominated Debt: An Empirical Examination. Journal ofBusiness, 2003,vol.76,no.4, The University of Chicago

Laporan Survei Manajemen Risiko Semester II (2011), Departemen Internasional, Bank Indonesia

Sharma, Somnath (2011). An Empirical Analysis of the Relationship Between Currency Futures and Exchange Rates Volatility in India. RBI Working Paper Series, WPS (DEPR): 1/2011, Department of Economic and Policy Research, Reserve Bank of India

Sahminan (2006), Adjustments of the Non-Financial Sector to the Rise in Exchange RateVolatility and Their Policy Implications in Indonesia. Working Paper No.WP/14/2006, Biro Riset Ekonomi, Direktorat Riset Ekonomi dan Kebijakan Moneter, Bank Indonesia.

309Risk Of Indonesian Banks: An Application of Historical Expected Shortfall Method

RISK OF INDONESIAN BANKS: AN APPLICATION OF HISTORICAL EXPECTED

SHORTFALL METHOD

Nevi Danila1

BunyaminSiti Munfaqiroh

Asian and European Crises were witnesses of banks’ vulnerable due to market risks. The Basel

Committee requires an internal risk assessment applying Value at Risk (VaR). However, a replacement

of VaR with Expected Shortfall (ES) has been suggested recently due to an excessive losses produced by

banks which are beyond VaR estimations. This paper studied the risk of Indonesian banks applying a

historical Expected shortfall. We used JIBOR (overnight) from 2009 – 2012 as a proxy of market risk. The

assessment of a historical expected shortfall of the net position of 27 banks accounts for October 2012

showed that state owned banks placed among the five highest value of each component (net position)

in the balance sheet, namely placement to Bank Indonesia, interbank placement, spot and derivatives

claims, securities, and loans. It means that the state owned banks had the highest risk and were the most

aggressive among Indonesian banks. It might be due to carrying some of the government’s program,

such as small enterprise loans.

Abstract

Keywords: expected shortfall, value at risk, banks, risk.

JEL Classification: D81, G210

1 Nevi Danila is a lecturer at Malangkucecwara School of Economics, Malang, [email protected]; Bunyamin is a lecturer at Malangkucecwara School of Economics, Malang, [email protected]; Siti Munfaqiroh is a lecturer at Malangkucecwara School of Economics, Malang, [email protected].


I. INTRODUCTION

The Asian Crisis of 1998 caused many financial institutions to go bankrupt. The crisis was repeated in 2008 due to a default of sup-prime mortgages in the USA. The crisis spread all over the world, especially in Europe. To anticipate the impact of the crisis, the Indonesian Central Bank (Bank Indonesia) enacted several policies in 2008. These included a liquidity injection, a domestic market expansion and a guarantee blanket for depositors (Economic Report on Indonesia, 2008). The global crisis provided a lesson to the Indonesian financial sector. Moreover, since the products provided by banks have become more sophisticated, it requires a stricter supervision to protect the needs of the general public.

The Basel Committee indicates that the current measure, capital adequacy ratio (CAR), is not enough to measure the banks’ risk. Therefore, an additional risk analysis tool, such as Value at Risk (VaR), is necessary. Further, Roulstone (1999) suggested that a market risk disclosure should be required, such as that used by the USA, the UK and International regulators. For this reason, VaR has been widely used for external reporting. In the USA, by the ends of 1990s, 30% of financial and non-financial firms had used VaR. Moreover, five out of six banks has applied VaR as a market risk measure method in UK (Woods, Dowd, and Humphrey, 2008). Further, the latest disclosure survey by Basel Committee reported that VaR has been a favorite market risk measure of International banks. The use of that measure has accounted for 89% of the information on market risk exposure (Basel Committee, 2003). Nevertheless, the Basel Committee suggested replacing VaR with Expected Shortfall (ES) to measure risk. This suggestion is due to the recent crisis which produced losses more than maximum losses suggested by VaR (Carver, 2012). In the Basel Committee on Banking Supervision Consultative Document, the associations support moving from VaR to Expected Shortfall (ES) because ES is expected to be more stable than VaR in measuring risk (Elliot and Miao, 2007; Letmark, 2010; Basel Committee, 2012). Expected shortfall is defined as the conditional expectation of the losses exceeding VaR (Munezon, 2010).

Currently, the Indonesian Banks are not using VaR as a market risk exposure method. We note above that International regulations imposes the use of VaR as an internal risk measure as well as for external reporting. For this reason, it is important to measure a market risk of Indonesian Banks using VaR. Estimating Indonesian Bank risk by employing Earning at Risk (historical simulation) has been studied by Muresan dan Danila (2005). However, Sinha and Chamu (2001) argued that risk estimation using historical simulation and riskmetrics methodology produced serious errors in the high fluctuation market. Further, VaR is an incomplete risk metric since it cannot provide any information about the magnitude of losses once the VaR limit is exceeded (Munenzon, 2010). Therefore, along with the Basel Commitee suggestion above, we examine the risk of Indonesian banks using a historical expected shortfall as an additional risk measurement method. It is our hope that Indonesian Regulators will require all Indonesian banks touse the Expected Shortfall method to report their market risk exposure.


II. THEORY

There are 120 commercial banks and 1837 rural credit banks in Indonesia. Looking at the big number of Indonesian banks, a market risk method for measuring the internal risk and external reporting is necessary. As a regulator, Bank Indonesia (Indonesia Central Bank) has to monitor Indonesian banks to ensure that they are running their businesses based on prudential principles. Bank Indonesia complies with Basel II which requires capital adjustment to credit risk and operational risk, and introduces changes in calculation of capital to cover exposures to risks of losses caused by operational failures (Bank Indonesia, n.d).

Bank Indonesia uses CAR to measure the risk of Indonesian banks. CAR is a bank’s capital measurement expreseed as a percentage of its risk weighted credit exposures (Reserve Bank of New Zealand, n.d). CAR is defined as (Tier 1 capital + Tier 2 capital)/Risk weighted assets. Tier 1 is a bank’s core capital, including equity capital and disclosed reserves. Tier 2 is a secondary capital of the bank. It includes undisclosed reserves, general loss reserves and subordinated term debt. Basel II requires banks to have its CAR minimum at 8%. Therefore banks are categorized as healthy if their CAR is above the minimum of 8%. There are many studies that examined risk in banking institutions. Beltratti and Stulz (2009) argued that banks with more Tier 1 capital and large deposits at the end of 2006 had high returns during the crisis. They also suggested that banks with more loans and more liquid assets had better performance after the bankruptcy of Lehman Brothers. Moreover, the banks from countries that had tight capital supervision and more regulations performed better than those that did not have such oversight.

Estrella, Park and Peristiani (2000) compared the affectivity of the model capital ratio to predict a bank’s bankruptcy. They concluded that employing simple ratios, such as a leverage ratio and a ratio of capital to gross revenue, they were better able to predict the bankruptcy of a company. The affectivity of the ratios was as good as other risk-weighted ratios to predict the bankruptcy in the short-term (one or two years). Thus, regulators should be required to include the ratios when they made regulations related to capital. However, risk weighted ratios were superior to predict a bankruptcy in the long term.

Furthermore, Dzeawuni and Tanko (2008) used capital adequacy, asset quality, management quality, earnings ability and liquidity (CAMEL) in measuring the banks’ performance. They suggested that CAMEL was not enough to measure banks’ performance. They recommended that regulators re-evaluate ratios of CAMEL in the performance measurement of banks. Furthermore, the DuPont analysis has proven to be quite promising to measure banks’ performance (Vensel, Aarma and Vainu, 2004).

The main business of traditional banks are short-term borrowing via deposits and long-term lending via loans. However, modern banks use money markets to facilitate the short-term borrowing, holding securities and long-term lending. Moroever, banks also enter the derivatives markets (Begenau, Piazzesi, and Schneider, 2012). All of the activities are exposed to market risk, namely foreign exchange risk, interest risk and stock market changes. In other words, the


value of a bank depends on its exposure to market risk. For example,the Assets and Liabilities sides of the Balance Sheet are exposed to market risk (Elsinger, Lehar and Summer, 2003). As we are aware, these market and other risks are extremely volatile.

It is suggested that banks with global operations consider calculating its capital in relation to its returns from diversification, such as VaR (Jackson, Maude and Perraudin, 1998). As mentioned above, banks borrow short-term in the money market via repurchase agreement, and lend long-term via holding securities. These activities are exposed to market risk. In most countries, commercial banks are required to disclose their trading risk, and VaR is the most popular method, especially a historical simulation (Perignon and Smith, 2008). The markets empirically have special features, such as returns that do not follow a normal distribution; losses and gains that are concentrated; and there is gain/loss asymmetry (Munenzon, 2010). Accordingly, a risk metric that focus on tail losses is important since it has a significant impact on portfolio performance. One such risk metrics is VaR. Nevertheless, Indonesian regulators do not require banks to disclose their trading risk information using VaR.

The use of VaR to measure the risk in banks trading operations has increased by major banking institutions. Jackson, et. al. (1998) argued that employing historical simulations gave more accurate measures of the worst expected loss than parametric methods due to non-normality of financial returns. Using the parametric method under-predicted the number of large losses, thus capital requirements needed to be adjusted frequently. Another study supports this finding, Nath and Samanta (2003) measured the Government of India’s bonds risk using variance-covariance, historical simulation and tail index based methods. Their results suggested that the historical simulation method gives more accurate VaR results than the variance-covariance method; while the tail-index approach gave overestimates of VaR results.

Muresan and Danila (2005) employed historical simulation to estimate Indonesian banks’ risks. The historical method keeps historical returns and losses within the portfolio. It assumes that the past history and performance will repeat in the future. However, the method does not accurately capture the risk of future events (Trenca, 2009). Moreover, Sinha and Chamu (2000) showed that estimating risk employing a historical simulation (Riskmetrics methodology) lead to errors in this volatile market. Perignon and Smith (2008) also showed that a historical simulation has very little information about future volatility of revenues.

2.1. Value-at-Risk

Value at Risk (VaR) has been widely used since J.P Morgan adopted this method in 1994. VaR measures the worst expected loss that an institution can suffer over a given time interval under normal market conditions at a given confidence level. VaR has three elements. They are a time period, a confidence level, and a loss amount (or loss percentage). Thus, VaR addresses questions, such as “what is the most money I can expect to lose over the next month (or next year) with a


2 It means that 95% of the time we would expect the maximum loss over a month (a year)

95%2 or 99% level of confidence?” (Butler, 1999).VaR estimates can be calculated for various types of risks, namely, market, credit, operational risk. There are two major families concerning VaR methodologies: the historical methods and the parametric methods (Dobranszky, 2009; Linsmeier and Pearson, 2000; Letmark, 2010; Damodaran, 2007; Berry, 2008). Artzner (1999) suggested that risk measures have to satisfy four axioms. These are translation invariance, sub-additivity, positive homogeneity, and monotonicity. These are called coherent, in order to be effectively used in managing risks.

Moreover, according to Damodaran (2007) and Munenzon (2010), there are some limitations of VaR. One of them is return distributions. Returns empirically do not show a normal distribution. Nevertheless, Delta-normal approach assumes that the distribution is normal and violation of this assumption will underestimate VaR. The Monte Carlo approach assumes a future probability distribution. However, the judgment made could be wrong. With the historical simulation, the assumption of distribution based on past data represents the forward-looking distribution. In addition, VaR fails to meet the characteristics of sub-additivity. This is, the risk of portfolio in terms of VaR may be larger than the sum of risk of its components (Letmark, 2010).

Elliot and Miao (2007) and Letmark (2010) suggested that Expected Shortfall (ES) or Conditional Value at Risk (CVaR) has superior properties than VaR. It optimizes market portfolios whether or not it follows a normal distribution (Trenca, 2009). Moreover, ES/CVaR satisfies four axioms of a coherent risk metric and has a convex function of the portfolio weights (Munenzon, 2010; Pflug, 2000), thus it can be used to manage risks effectively. ES/CVaR is the expected loss incurred in the % worst cases. ES/CVaR is defined as the conditional expectation of the losses exceeding VaR (see Letmark, M, 2010 for detail of ES or CVaR). Mathematically, ES (from this point onward, we use ES) can be defined as:

ES = -E(R | R < -VaR)

III. METHODOLOGY

The balance sheet items are exposed by market risk, e.g. short term government, bonds and receivables loans to other banks, loans to non-banks, bonds, stock holdings, other banks liabilities with non-banks, securitized liabilities which are exposed to interest rate risk, stock price risk and foreign exchange risk (Elsinger, Lehar, and Summer, 2003; Mehta, Neukirchen, Pfetsch, and Poppensieker, 2012). In addition, Woods, et al (2008) suggested that banks are exposed to market risk in many ways, for example, banking profits would be squeezed by narrowing margins between loans and deposits, and the bad debt would be increased if there is an increase in interest rate.


3 Trenca (2009) used foreign exchange data as a market risk and applied VaR to measure the risk of banks’ foreign currency net position.

4 Arikunto (2006) stated that when the population is less than 100, then we should include all observations, otherwise we may choose a sample size of 10%-15% or 20%-25%, or more. (p.134). Our population size is 120, and we take a sample size of 27.

In this paper we used Jakarta Interbank Offered Rate (JIBOR) as a proxy of interest rate risk. JIBOR is an indicative rate used for money market transactions. This indicative rate refers to unsecured loan transactions in money markets, which reflects the interest rate that banks charge each other for taking and offering loans. This rate is then published and serves as the benchmark for market transactions. JIBOR is expected to become a credible reference rate that will be widely used for financial transactions in Indonesia (Bank Indonesia, n.d)

In our study, we used the overnight JIBOR from 2009 – 2012. Following Abbasov (2012)3, we calculated the net position of each bank’s accounts in October 2012’s balance sheet. For example, net position of placement to bank Indonesia defined as placement asset to bank Indonesia at asset side – placement asset to bank Indonesia at liabilities side. We used 274

Indonesian Banks as sample, including state owned banks, foreign exchange banks, regional banks, joint venture banks, and foreign banks. We estimated the risk of banks using a historical ES.

There are several steps performed in assessing the Indonesian banks risk, as follows:

1. First, we calculated a net position of balance sheet account, namely placement to Bank Indonesia, interbank placement, spot and derivatives claims, securities and loans.

2. Second, we calculated a historical ES at 99% confidence level of JIBOR using Performance Analytics package.

3. Finally, we multiplied the historical ES with a net position of balance sheet account for each bank. For example, the historical ES of “X” Bank = -2.88%; net position of “X” bank loans account = Rp. 100,000,000 millions. Then, the ES value of net position of loans account = Rp. 100,000,000 millions x -2.88% or Rp. 2,880,000 millions


The value of historical ES means a largest possible overnight loss of the bank’s account net position (because we used overnight JIBOR). For example, the net position of Mandiri bank loans account is Rp. -9,876,693 millions. This means the largest possible overnight loss of loans account is Rp. 9,876,693 millions. A positive value of ES net position indicates that the bank’s liabilities were larger than its assets, e.g. the net position of DKI bank’s loans account was positive and indicated that a time deposit (liabilities side) was larger than loans (asset side). It means that the expectation of time deposit losses exceeded the expectation of loan losses.


At a glance, table 1 - 5 on appendix showed that state owned banks dominated the top five largest ES of bank’s account net position; except a spot and derivatives claims account (only BNI was included in the top five largest ES). It implied that they had the highest risk. In other words, the state owned banks were more aggressive. This might be due to carrying some government’s program, such as small enterprise loans. While BCA was the most aggressive bank among foreign exchange banks in Indonesia, the foreign banks placed the highest rank for derivatives claims. The reason might be the familiarity of the products. On the other hand, regional banks did not have derivatives claims. It might be due to a prohibition from a local government. In addition, the amount of money placed in Bank Indonesia by majority of foreign banks was much smaller than state owned banks.

Furthermore, loan accounts had the highest ES among others accounts. This implies that the main business of banks in Indonesia were still conventional (loans). The state owned banks which have securities companies tend to trade their money in the securities. This is shown from the five highest ES of securities accounts dominated by the state owned banks, namely Mandiri and BNI. Moreover, the securities accounts had the second largest value of ES after the loans account. It suggested that the banks used capital markets as a second business. It indicates that the Indonesian capital market was the more common place to invest.

Abbasov (2012) used the average interest rates of loans as the risk factor in calculating VaR. He applied it to the gap of assets and liabilities of 32 banks in Azerbaijan. The maximum unexpected losses noted from the assets and liabilities gap is not more than 26.1 million manat.

Table 6 shows that the four highest ES of banks have similar CARs; in the range of 14 to 16. The CAR places the third to fifth lowest of all sample banks. Nevertheless, they are at the first three highest of ROA and of ROE. It follows the rule of high risk, high return. Furthermore, we can say that ES has a similar conclusion to financial ratios. It implies that ES is effective for measuring the bank’s risk.

In summary, we can use ES to measure the risk of banks as suggested by Basel II. Moreover, we conclude that the state owned banks had the largest risk among others banks.

V. CONCLUSION

Expected Shortfall (ES) has not been commonly used for assessing the risk of banks in Indonesia. This paper enriches the existing risk assessment of Indonesian banks, such as CAR and profit/loss analysis. Moreover, this paper also tried to provide an answer to the Basel II requirement for employing ES in assessing banks risks. ES calculates an expectation of the losses exceeding VaR. Using a sample of 27 Indonesian banks we argued that state owned banks had the largest value of ES in a bank’s account net position, except for spot and derivatives claims accounts. It implied that state owned banks were the most aggressive banks among others. In summary, state owned banks were the riskiest banks among others banks in Indonesia.


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APPENDIX

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325Persistency and Sustainability of Indonesia’s Current Account Deficit

Persistency and sustainability of indonesia’s current account deficit

Tuti Eka Asmarani1

This paper is motivated by the current account deficit in Indonesia’s, notably since 2011Q4. The

aim of this paper is to test if this deficit is persistent and sustainable. Using stationary and Autoregressive

Distributed Lag (ARDL) approach, the result shows the Indonesian current account deficit is persistent for the

period of 2011Q4-2014Q1, and is considered to be unsustainable. These two findings call the government

to optimize the policy on supporting the export performance, and transport services in particular.

abstract

Keywords: Current account deficit, random walk, intertemporal budget constraint, unit root, ARDL

JEL Classification: C22, F32, F41

1 Author is graduated from Postgraduate Program, Department of Economic, University of Indonesia, and is a lecturer at University of Gunadarma ([email protected]).


I. INTRODUCTION

Current account balance is one important indicator in determining a country’s macroeconomic performance on the external side, which is also a reflection of the internal economy, such as exports and imports in the real sector, as well as revenues and expenditures in the fiscal sector (government). When the current account balance is positive (surplus) it means that the country lends its excess savings abroad, so that the stock of net assets increased. When the current account balance is negative (deficit), this implies the country has underfunded savings for domestic investment, so it must borrow from other countries.

Deficit / surplus of the current account balance can lead to gains and losses in the long-term and short-term. The current account surplus in the short-term is a benefit, because the state will earn revenue in the form of interest on the loan from the investment abroad, but it is a disadvantage in the long-term, because the stock of domestic savings would be reduced as a result of their investment abroad, particularly if on a large scale. As a result, the country’s development can become impaired. A deficit is beneficial in the short-term, as the loan / foreign debt can be used to finance domestic investment, but it is detrimental in the long-term because the current account deficit could lead to a serious economic crisis (Polat, 2011).

Figure 1.The ratio of the current account to the GDP of Indonesia,

Period 2004Q1-2013Q2

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From Figure 1, it can be seen that Indonesia experienced a current account deficit for twelve quarters after the economic crisis of 1997. Among other things, in the 2004 first quarter, the current account deficit was due to an increase in oil import activities accompanied by an increase in the domestic oil prices (Balance of Payments Indonesia, 2004). In 2005, Indonesia again experienced a current account deficit in the third quarter. This was caused by the high acceleration of growth in non-oil imports (raw materials and capital goods) due to an increase


in domestic demand (Bank Indonesia report to Parliament Q3, 2005). After some recovery time lapsed, a current account deficit was experienced in the second quarter of 2008. The deficit occurred due to a significant decrease in the trade balance. Inevitably, this deficit continued until the third and fourth quarter of 2008. Bottom ranking in the current account was caused by the global crisis in the United States that resulted in global uncertainty (Balance of Payments Indonesia Quarter I-IV 2008).

Another current account deficit occurred in Indonesia in the fourth quarter of 2011. This current account deficit lasted until Q4 2012. Sluggish export performance due to falling global demand became the drivers of this deficit. Bank Indonesia predicted a current account deficit the second quarter of 2013 valued at US $ 9 billion. However, this prediction was missed because the current account deficit in the second quarter of 2013 reached up to US $ 9.8 billion.

The BI misprediction was due to growth in export performance which turned out to be lower than previously thought. This was evidenced by the decline in exports of passenger transport services due to the many national travelers who traveled for vacation abroad, and the rising freight transportation services caused by an increase import activity (Balance of

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Payments Indonesia, 2012-2013). In addition, Indonesian imports, mainly oil and gas had not yet experienced a significant decline, although the price of fuel oil (BBM) had increased (Balance online, 2013). This condition is reflected in table 1.

From Table 1, the merchandise trade balance in oil commodities experienced a deficit with an increasing trend over the period 2011-2013. However, due to the large amount of exports of non-oil and gas commodities, the deficit in the trade balance could still be overcome. However, with a decline in export activity in the gas commodity, a merchandise trade balance deficit in the second quarter of 2013 was unavoidable.

The Asian Development Bank (ADB) also estimated the Indonesian current account deficit would last throughout 2014. The Deputy Country Director of ADB’s Indonesia Resident Mission, Edimon Ginting, saw the current account deficit because of the weakening of the global economy (Business News, 2013). If the current account deficit persisted and there was no intervention from the government, it was feared it would lead to a crisis, as happened in Chile and Mexico (early 1980), the United Kingdom and Norway (end 1980), Mexico and Argentina (mid 1990), the countries of East Asia (late 1990), and Turkey (early 1994 and early 2001). Therefore, many countries want a non-persistent the current account deficit (mean-reverting), because when a country experiences a deficit that is persistent, it will create a rebalancing, in which the country is continuously importing while other countries supply the needs of its imports. Naturally, this will lead to inequality in the welfare of the world community. Besides not wanting a non-persistent current account deficit, many countries also want an account balance under sustainable conditions.

Mean-reverting is a condition in which the current account deficit does not last long. That is, these deficits will eventually return to long-term equilibrium. If the deficit is not returned to long-term balance due to a shock, then the event is called a random walk phenomenon. Lau et al. (2006) found that the current account balances in the Asia-5 (Indonesia, Korea, Malaysia, Philippines and Thailand) for the period 1976Q1-2001Q4 were mean-reverting. The sustainability of the current account refers to the willingness and capability to finance a range of activities, such as imports and foreign debt interest payments, through export activity. Thus, the current account of a country can be said to be sustainable if revenue from exports is able to finance imports and liability payments for foreign debt interest. Or in other words, the sustainability of the current account of a country is a reflection of the cointegration relationship between exports and imports and foreign debt interest (imports plus). That is, exports and imports plus move together toward a long-term balance point, so that the current account balance is created. This could occur because of the implementation of macro-economic policies that are effective in the long-term. Therefore, it can be concluded that a current account deficit is only a short-term phenomenon that will eventually return to a long-term balance (Perera and Verma, 2008).

The Cointegration approach that explains the relationship between exports and imports plus, is often called intertemporal budget constraint (Husted, 1992). This approach allows a


country to borrow and lend funds to the international market in maximizing satisfaction based on the budget constraint. This means that if a country wants to apply for foreign loans, then the country should consider the condition of the rest of the loan at the moment, where net borrowing abroad from a country should be equal to the present value of the overall current account in the future, or in other words, the loan must be equal on par with the current account in the future as assessed at the current time.

Currently there is no research specifically addressing the sustainability of the current account in Indonesia using the model intertemporal budget constraint. Baharumshah, Lau and Fountas (2002) conducted a study on the sustainability of the current account deficit in the four ASEAN countries, namely Indonesia, Malaysia, Philippines and Thailand. As a result, all countries except Malaysia have not had a sustainable current account. The reason, in the study period 1961-1999, there was the the Asian crisis that hit most of the ASEAN countries. The crisis that occurred in 1997 and 1998 led to sluggish domestic economies in the ASEAN countries, so that the external imbalances were unavoidable.

In line with the research that was done by Rahman (2011), the sustainability of the current accounts in Malaysia (1960-2007) and Indonesia (1960-2008) found that the current account in Indonesia was still in a state of unsustainable until 2008. According to him, after the 1997/1998 crisis, Malaysia was better able to manage the balance of the trade deficit compared to Indonesia.

Therefore, in this research will discuss the persistence and sustainability of the current account balance in Indonesia during the start of the current account deficit from 2004Q1 to 2014Q1. The persistence of the current account will be explained through the unit root test, while sustainability will be explained through the intertemporal budget constraint approach by identifying the cointegration relationship between exports and imports plus her. There are several methods that can be used to explain the cointegration relationship. Among them, are the Engle-Granger method, Johansen method, and Autoregressive Distributed Lag (ARDL) method.

The Engle-Granger method describes a long-term relationship using an estimation residual. If the residual is stationary, then the export and import plus have cointegration relationship. However, before the residual is estimated, the exports and imports plus variable must first have the same stationarity. Stationarity can take the form level or I (0) and the first difference or I (1). Just as the Engle-Granger method, Johansen method also requires that all variables have the same degree of stationary prior to the cointegration test. As for the method of Autoregressive

Distributed Lag (ARDL), this method allows the cointegration of a number of variables that have a different degree of stationarity, making it is more efficient in the testing. In addition, the ARDL method is a method that is not only able to explain the long-term relationship, but also able to take into account the short-term dynamics, making it very suitable for use in small-sized samples. This method has been used by Hassan et al. (2012) to analyze the sustainability of


(2)

(3)

(4)

the current account in the Iranian state. Using this method, Iran’s current account deficit was found to be in a sustainable condition.

If it is found that Indonesia has a current account deficit that is non-persistent, and it is an unsustainable current account, this research would provide clarity / confirmation of previous researchers. Hopefully, this study can answer the challenges ahead regarding the ability of Indonesia’s current account balance in maintaining an external balance in the midst of the global economic climate of uncertainty.

The second section of this paper presents the theory and review of the relevant literature. The third section outlines the data and processing methods of testing. The more technical explanations of the model and methodology are presented in the appendix. The fourth section reviews the results of data processing and analysis, while the conclusions and policy implications are outlined in the fifth and concluding section of this paper.

II. THEORY

2.1. The Persistence of The Current Account

The persistence of the current account deficit is a condition where the current account deficit takes place continuously. This can be shown by the following equation:

Equation (1) explains that the expected current account in the current period (t) is a reflection of the current account in the previous period (t-1). In forecasting, there are random factors that can change at any time. The random factor is called the error. Error / error in

forecasting can be calculated by the following equation:

(1)

Equation (4) shows the phenomenon of random walk in which the value of the current account at the current time (t) is equal to the value of the current account in the previous period (t-1) plus a random factor (Clower dan Ito, 2005).


(5)Ct = Yt + Bt – It – (1 + rt)Bt-1

where Ct is the current consumption, Y

t is revenue, B

t is net loans (debts), I

t is an investment,

and (1 + rt) B

t-1 is net lending for the previous period. As is already known that the identity equation is Y

t = C

t + I

t + X – M, where X-M is the Current Account (CA), or Y

t – C

t – I

t = X

t – M

t

= CAt. Then:

2.2. Sustainability of the Current Account

This research will apply intertemporal budget constraint models that explain the relationship of exports and imports plus for the long-term balance (Husted, 1992). To simplify the discussion, the assumption built is that the economy is open and has a relatively small size, where there is only one type of goods produced and exported. It is also assumed that there is no government intervention, that the state can borrow from and lend funds to the international market, and that the interest rate used is the world interest rate. The last assumption used is that agents are rational and maximize satisfaction all the time with a certain budget constraints (budget

constraint).

The budget constraint equation in period t is as follows:

where

Equation (6) shows that net lending abroad (Bt) in a country’s economy in period t is the

present value (PVt) of the overall current account in the future, or in other words, the loan must

be on equal par with the current account in the future as assessed at the current time. If Bt >PV

t

then the country will be back into debt to pay the remaining foreign debt. However, if Bt<PV

t

, then the country returns to debt to increase domestic investment because of the perceived debt in the previous period brings many benefits.

The concept of intertemporal budget constraint above are used in describing sustainability/ sustainability of the current account balance which is reflected through the cointegration relationship between exports and imports plus, which is shown by the following equation:

(6)

(7)

Equation (7) shows that the export in the period t (Xt) has a cointegration relationship

with imports plus (imports are added with accrued interest) in the period t. That is, if the exports and imports plus are cointegrated, then they will move together towards long-term balance (Jain and Sami, 2012).


2.3. Literature Review

Research on the persistence of the current account is still rarely performed. Lau et al. (2006) conducted a study of the current account balance in the Asia-5 (Indonesia, Korea, Malaysia, Philippines and Thailand) for the period 1976Q1-2001Q4 and found that the current account deficit in the Asia-5 to be mean-reverting/non-persistent. In contrast with Lau et al. (2006), and Clower and Ito (2012) found that the current account deficit in the developed and developing countries to be experiencing persistence. The study was conducted in the period 1960Q1-2010Q4 in 71 countries, including Indonesia. Research on the sustainability of the current account balance through the identification of the cointegration relationship between exports and imports plus was done in various countries. Utkulu (1998) and Celik (2011) conducted a study on the sustainability of the current account of the Turkish state. Konya (2009) examined a similar case in countries Czech Republic, Hungary and Slovenia. Another case with Perera and Verma (2008) which analyzed the sustainability of the current account in the country of Sri Lanka, Yin and Hamori (2011) in the country of China, as well as Hassan et al. (2012) which analyzed the sustainability of the current account in the country of Iran.

All researchers sought to identify cointegration in explaining the relationship of exports and imports plus in the long-run, however each researcher modified their analysis. Utkulu (1998), and Celik (2011) used the method of Engle-Grenger in explaining this relationship. The Engle-Grenger method used a single equation engineering approach (single-equation approach) that had only one cointegration result. While Perera et al. (2008), Konya (2009), and Baharumshah, Lau and Fountas (2002) analyzed the sustainability of the current account balance using the Johansen Test. This method allows more than one cointegration result. If there is cointegration more than one, then the method of Engle-Granger can be misleading. However, in testing with Johansen Test, sometimes the results contained ambiguity, because of the different grades the Trace Statistic and Max-Eigen for showing the relationship cointegration. In addition, the weakness of the Engle-Granger and Johansen methods is the requirement that the data series should have a same degree of stationarity, namely at the level I (0) or I (1), and both methods do not take into account the dynamics of the short-term to the long-term balance. In another case, the Hassan et al. (2012) study analyzed the sustainability of the current account balance using the Autoregressive Distributed Lag (ARDL) method. The ARDL method allows the cointegration of a number of variables that have a different degree of stationarity, so it is more efficient in testing and more consistent in showing a cointegration relationship, for their long-term and short-term coefficient(s). Additionally, ARDL is well matched for a small sample. Therefore, in this study ARDL method will be used which is considered the state-of-art from the previous studies.


III. METHODOLOGY

3.1. The Persistence of the Current Account

1) Unit Root Test Augmented Dickey Fuller (ADF)

This test examines the stationary data that will be used to test the persistence of the current account. To facilitate an understanding of the root unit, consider the following model:

(8)

(9)

CAt = CA t-1 + εt.

Change the equation (8) into the empirical econometric equation that contains the unit

root as follows:

CAt = β1 + ρCAt-1 + εt

If ρ = 1 then the model becomes a random walk without trend. Hence, from this appears a problem with variants CA

t, so that CA

t has a data “unit root” or not stationary, that is because stationary data have value ρ with the magnitude -1< ρ <1. If the equation (9) above minuses CA

t-1 on the right and the left, then the equation becomes:

CAt – CA t-1 = β1 + ρCAt-1 – CA t-1 + εt

∆CAt = β1 + ( ρ– 1) CA t-1 + εt

Or it can be written by:

(10)

(11)

∆CAt = β1 + CA t-1 + εt

If adding the trend to the equation (10), the equation becomes as follows:

∆CAt = β1 + β2t + CA t-1 + εt.

Equation (11) assumes that εt is not correlated with the dependent variable. In anticipation

of such a correlation, Dickey Fuller developed the test by adding a lag on the dependent variable. This test is called the Augmented Dickey Fuller (ADF), shown as follows:

(12)

If translated, then the formulation is,

∆CAt = β1 + β2t + CA t-1 + + + ... + + εt


(14)

(13)

If the hypothesis cannot be denied, then ρ=1, means the data CAt contains a

unit root or is not stationary. But if the hypothesis can be rejected, it means that the data CAt

does not contain a unit root or is stationary. Acceptance or rejection of a hypothesis needs to be tested individually through the t test as follows:

an estimate of the and is the standard deviation of the estimated sampling (standard error). If the value of t-statistic is greater compared with the critical value, it can be concluded that the data does not contain a unit root or is stationary (and vice-versa).

2) Philips-Perron Test

Gujarati (2003) stated that Phillips and Perron test can anticipate the existence of serial correlation in the error term without having to add lag difference terms, as was done previously in equation (12). This test is only tightening the t test to increase the power of stationarity of the data. This can be illustrated as follows:

∆CAt = β1 + CA t-1 + εt

By adding the trend to the equation (14), the equation becomes as follows:

(15)∆CAt = β1 + β2t + CA t-1 + εt

In the ADF, coefficient is tested with t in equation (15), whereas in the Phillips-Perron coefficients tested with the following t:

where γ0 =(T-k)s2 / T. γ0 is a consistent estimate, T is number of observations, k the number of regressors, and s is the standard error of the regression. f0 an estimator of the residual spectrum at zero frequency. f0 used in this study is based on Bartlett Kernel with Newey-West as its optimal bandwidth, and choice f0 already available on the application Eview 6.0 (Eviews 6

where ∆CAt-1 = (CA

t-1 - CAt-2). From the equation above, the following hypotheses can

be made:


(16)

where zt is a vector of X

t and MM

t; constants μ= [μ

X,μ

MM]; and λ

i is a matrix of parameters

in the VAR lag i. Based on the equation (16), then a Vector Error Correction Model (VECM) can be developed as follows:

Users Guide). Another advantage of this test is its ability to capture the existence of structural breaks that occur in the majority of macroeconomic data (Enders, 2004).


Based on Pesaran et al. (2001), a modified ARDL, an Autoregression Vector model (VAR) of order p, is denoted by VAR (p), which functions as follows:

Following Pesaran et al. (2001), Xt must be in the form of a first difference variable or I

(1), whereas regressor MMt can be either level I (0) or first difference I (1), so that the model

of this study is as follows:

(17)

(18)

(19)

Coefficient β1 dan β2 represents the long-term dynamics of the model, while the coefficient β3 dan β4 represents a short-term relationship from the model. From the equation above, we can make a hypothesis as follows:

If the hypothesis H0: β1 = 0 and β2 = 0 cannot be denied, it means that there is no relationship between exports and imports plus in the long-term. Meanwhile, if the hypothesis can be rejected, it means that there is a long-term relationship between the variables exports and imports plus. Which hypotheses are met will depend on the final results of the F test statistics compared with the critical values shown in the table CI (iii) and CI (v) in Pesaran et


al. (2001). Referring to Pesaran and Pesaran (2001), the critical value of the lower limit (lower

bound critical value) assumes that the independent variable is cointegrated on the order of zero or I (0). While the critical value of the upper limit (upper bound critical value) assumes that the independent variable is cointegrated in order one or I (1). Therefore, as the F statistic is greater than the critical value (either the lower or upper), it can be concluded that there is a long-term relationship between exports and imports plus. However, if the F statistic gained in value is between the lower and upper, then the results are inconclusive (Fuso and Magnus, 2006).

IV. RESULTS AND ANALYSIS

4.1. The Persistence of the Current Account

As was explained earlier, the persistence of the current account deficit can be illustrated by the phenomenon of random walk, where the deficit is strongly influenced by the deficit at an earlier time and a random factor / error term. This phenomenon can be captured through the stationary test Augmented Dickey Fuller (ADF) and Philip-Perron. The current account that follows the random walk pattern will generate data that are not stationary or contain a unit root. Non-stationarity is called persistence. In contrast, the current account that does not follow the pattern of a random walk or so-called mean-reverting, will produce stationary data. The stationary data has the probability to be controlled for stability at any time if exposed to shock, so it does not cause permanent or persistent impact (Lau et. Al., 2006). The ADF stationarity test and Philip-Perron test for the Indonesian current account 2004Q1-2014Q1 period is shown in the following table:

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Based on the above stationary test, it can be concluded that Indonesia experienced a deficit in the current account due to global imbalances that penetrated into the domestic economy, as is evident by the patterns formed by the stationary test as follows:

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The global imbalances stemmed from the crisis experienced by the United States (US) in 2008. As one of the superpowers, this crisis did not only affect the condition of the American economy, but also the economy in other countries, including Indonesia. The influence of the crisis was transmitted through two routes, namely the financial channel and the trade lanes. From the financial channel, institutions or financial institutions that have assets in insolvent companies were directly affected. In addition, due to the crisis, many foreign companies pulled their funds out of Indonesia in the stock market, due to liquidity problems in the country.


Concerning trade, the demand for American imports from Indonesia decreased because of sluggish US consumer purchasing power due to the crisis (Listiarso, 2013).

The crisis experienced by the United States happened because of their bad loans to the subprime housing that started in 2007. Ultimately, large financial institutions, credit providers, Lehman Brothers, went bankrupt in 2008, which then had a domino effect throughout the world, including Indonesia. This caused a current account deficit Indonesia in 2008. However, the current account deficit of that year was not a persistent condition due to the recovery in 2009 (Indonesia Economic Outlook, 2009).

In 2009, the American economy showed improved performance as a reflection of the effectiveness of policies for the economic rescue package of US 838 billion made by the government and the Federal Reserve. The recovery of the US economy had implications for the increasing the flow of investments into the domestic capital market and increased demand for American imports from Indonesia. Thus, the current account was back in surplus by 2009 to the third quarter of 2011 (Balance of Payments Indonesia Quarter IV, 2011).

It was not long after recovery that Indonesia once again was affected by the crisis in Europe at the end of 2011. Just as the US crisis, the transmission effect of the European crisis was felt by Indonesia through two channels, namely the trade and financial channels. For trade, the weak purchasing power of the people of Europe reduced the demand for goods, so that Indonesia’s exports also dropped. While the impact felt through the financial channel was a distrust of global investors to invest in European countries due to the large ratio of debt to GDP of these countries, which caused these global investors to look for safe places to put their funds. Indonesia was included as one of the objectives of the global investors. As for the investments, a lot were made in the form of portfolio investments. This capital inflow led to a strengthening of the rupiah, bringing many dollars into Indonesia. Strengthening of the rupiah led to a decline in exports as the domestic commodity prices were considered expensive by the overseas markets. This caused a persistent current account deficit experienced by Indonesia in the first quarter of 2014.


The initial procedure used the Autoregressive Distributed Lag (ARDL) method to determine the optimal lag using the Schwarz Information Criterion (SIC). Based on the SIC, the optimal lag that can be used in this research was the lag 1. However, Pindyck and Rubinfeld (1991) suggested to run the model with several different lags and ensure that the results obtained were not sensitive to the length of lag. Thus, in this study, four lags were used to test the sensitivity of the results. After getting the optimal lag, then the cointegration test of Autoregressive Distributed Lag (ARDL) was done. The results are as follows:


Based on bound testing at level I (0) and the first difference or I (1), it was concluded that the exports and imports and have no cointegration relationship. Or in other words, both these variables do not have a mutual balance in the long-term. The absence of a relationship in the long-term between exports and import plus and the current account shows Indonesia in a state of unsustainability, for both goods and services. That is, Indonesia is not able to finance imports and foreign debt interest through export activities, which creates a deficit in the current account. This unsustainable condition occurred in 3 (three) years, i.e., in the fourth quarter 2011 to the first quarter of 2014. A deeper, unsustainable condition occurred in the service sector.

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Figure 2. Services Deficit Balance Indonesia According to Husted period 2004Q1-2014Q1


The transport services had the largest deficit of the services sector, particularly for the transportation of goods due to high domestic oil imports, as seen in the following figure:

Figure 3.Services Balance of Transaction Period 2011Q1-2013Q3

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Bank Indonesia sought to reduce the deficit in the current account to maintain its main policy instrument, the BI Rate -- if the BI Rate is increased, then the interest rate differential of domestic and foreign interest rates would also widen. This is what will encourage foreign investors to invest in financial instruments in Indonesia, because the investors will get a higher rate of return, which would increase capital inflow. However, an increase in capital inflow would increase the supply of dollars in Indonesia. This would cause the exchange rate to strengthen / appreciate. The appreciation of the exchange rate would result in lower prices of imported goods, and exported goods abroad would become more expensive or less competitive, so it will encourage imports and reduce exports. A fall in exports leads to reduced domestic production, so that the prices of goods would increase and economic growth would slow down. This is a fact that must be faced in the future and cause vulnerability in the current account. The results of this study are consistent with the two previous researchers, namely Baharumshah, Lau & Fountas (2002) and Rahman (2011), that stated, the Indonesian current account is in unsustainable conditions. This condition is vulnerable to shocks created by the turbulence of the world economy into the domestic market (as examined by Baharumshah et al. for the Asian crisis of 1997/1998, Rahman for the American crisis of 2008, and this study for the European post-crisis, 2011).


V. CONCLUSION

This paper tested the existence and sustainability of the current account deficit in Indonesia. These results showed persistent deficits on the period 2011Q4-2014Q1, due to the influence of the European crisis that occurred in the fourth quarter of 2011. The transmission effects of the European crisis was felt by Indonesia through two channels, namely the trade and financial channels. The impact of the crisis on the trade lanes were not perceived directly, because Indonesia is not a major trading partner for the countries in Europe that were experiencing pressure. However, transmission through the financial channel was in the form of portfolio investments that caused the current account deficit bringing about the strengthening of the rupiah.

The second conclusion of this paper is that the current account in Indonesia is in an unsustainable condition. The condition is caused due to an unsustainable current account deficit which occurred in the fourth quarter of 2011 to the first quarter of 2014. The deficit was mostly contributed by the deficit in the balance of services, particularly transportation services for the amount of oil imports. The results of this study are consistent with the two previous researchers, namely Baharumshah, Lau & Fountas (2002) and Rahman (2011) which stated that the current account in Indonesia is in an unsustainable condition. This condition is vulnerable to shocks created by the turbulence of the world economy into the domestic market (as examined by Baharumshah et al. for the Asian crisis of 1997/1998, Rahman for the American crisis of 2008, and this study for the European post-crisis, 2011). Referring to the results above, it is necessary to optimize the four policy packages that have been launched by the government in August 2013, particularly a package of measures to encourage exports for addressing the current account deficit. In addition, policy for repairing the account deficit for services, especially the goods transportation services, with the provision of adequate transport facilities for import-export activities.

This study only uses the period 2004Q1 to 2014Q1 due to the limited availability of data. Therefore, it is suggested that future studies use data for a longer period in order to see the level of persistence and sustainability in the period before 2004. In addition, these conditions should be compared to other ASEAN countries.


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aPPendiX

The Concept of Inter-temporal Budget Constrain (Husted, 1992)

The equation of the budget constraint on period t is:

Ct = Y t + B t – I t – (1 + r t) B t-1

For Ct is current consumption at period t; Yt is income, Bt is net loan (borrowing - lending), It is investment, and (1 + rt) Bt-1 is net loan on previous.

As indicated on identity equation Yt = C

t + I

t + X – M, for X-M is Current Account (CA), hence

Yt – C

t – I

t = X

t – M

t = CA

t.


The Sustainability for Current Account

With simple manipulation, we get:

The above equations are equal to the followings:


Get the limit of Bt+n

:

We can rearrange the above equations to:

Now we get:

Assume Xt and Zt follows a random walk with:


then:

349The Effect of Ownership and Global Crisis to Income Diversification of Indonesian Banking

The effecT of ownership andGlobal crisis To income diversificaTion

of indonesian bankinG

Murharsito1

This paper attempts to examine the effect of ownership and global crisis to income diversification of

Indonesian Banks during period of 2005 to 2012. The income diversification is classified as the taxonomy

of De Young and Rice (2004), therefore income diversification is divided in to non-traditional stakeholder

non-interest income and traditional and fee for service non-interest income. Apart of regress the whole

bank sample, analysis is conducted to each of ownership types as well. Using pooled effect panel data,the

study result suggests that ownership doesn’t affects income diversification of Indonesian banks both

to the non-traditional stakeholder and traditional and fee for service non-interest income. However, the

direction effect of public ownership is negative in both non-traditional incomes in contrast direction of

foreign ownership is positive. Then, in each types of ownership, capitalization affect significantly in positive

direction to non-traditional stakeholder non-interest income. In terms of traditional and fee for service, in

state banks, size has a positive and significant effect. In foreign banks credit risk affect in positive direction,

but it affect oppositely in private banks. In addition profitability also affects positively to traditional and fee

for service non-interest income in private banks. The effect of global crisis has different direction to each

non-interest income, it encourages banks to generate traditional and fee for service non-interest income.

And although it is not significant, it has negative effect to non-traditional stakeholder non-interest income.

abstract

Keywords: Income diversification, ownership, global crisis, Indonesian banks

JEL Classification: G01, G15, G21

1 Lecturer at Nahdlatul Ulama’ Islamic University (UNISNU) Jepara, email: [email protected].


I. INTRODUCTION

Indonesian banking sector is about to be controlled by more diverse owners recently, the domination of state banks is gradually decreased, on the other hand private and foreign banks increased their role participating in Indonesian banking sector. According to the total asset data, at the end of 2002, state banks comprise 46. 44%, private banks (both foreign exchange and non-foreign exchange private banks) comprise 37. 30%, and foreign and joint venture banks comprise 11. 02% of banking sector, ten years later at the end of 2012 those composition shift significantly, state banks comprise 36. 02%, private banks (both foreign exchange and non-foreign exchange private banks) comprise 43. 19%, and foreign and joint venture banks comprise 12. 19% of banking sector (Bank Indonesia, 2004, 2012). The 1998 banking crisis that hit this sector seriously was then followed by privatization programs which invited foreign investors to participate in this sector, Sato (2005) stated that the combination of bank branches, foreign joint banks and foreign owned private banks accounting for 31 % of bank asset as a whole, up from 9% before the crisis occurred. This condition has intensified the competition among banks and forces them to maximize their efforts in order to increase their market share. Income diversification is one strategy that could be applied by banks to improve their profitability. Banks could extent their business not just in their traditional activities such as loan making but also create and develop non-traditional activities, such as service commission, trading revenue, insurance fee etc. In Indonesian banking, the role of income diversification has been realized as one of the important corporate income sources, study by Sufian and Habibullah (2010) about Indonesian banks profitability over 15 years period concluded that income diversification plays a key role and has a positive relationship with the banks profitability.

Further issue that is interesting to be discussed is whether the ownership factor has the effect to the income diversification to the non-traditional sectors by Indonesian banks. As the owners of banks is more diversified among state, private and foreign owner, and each of them has significant portion in Indonesian banks market share. The ownership difference could lead to the performance difference, many studies have revealed the effect of the relation between firms ownership to its performance, the result of those researches mostly highlight the weak performance of state owned enterprises compared with the others (Hart et al. , 1997; Shleifer and Vishny, 1997; Dewenter and Malatesta, 2001). Specifically, the effect of the ownership to banks performance also reveals similar result. Cornett et al (2010) concluded that state owned banks generate less profit, not well capitalized and more risky in the term of credit than private banks prior to 2001. Beck et al (2004) concluded that higher share of state owned banks make the effect of bank concentration acerbated, while foreign banks presence prevent the effect of bank concentration on credit obstacles. Bonin et al (2005) concluded that in terms of efficiency, foreign owned banks are the most efficient while state owned banks in the opposite place become the least efficient. If this theory also materializes in terms of bank achievement on non-traditional income activities, there will be a difference on the non-interest income among state, private and foreign banks. Recent study on Indian banking industry by Pennathur


et al (2012) indicate that different type of ownership will cause different impact on income diversification, further public sector banks earn the lowest fee income while foreign banks can generate higher fee income.

On the other hand, the occurrence of financial crisis which hit world economies more frequent currently could also affect banks performance, particularly in generating non-interest income. Recently, there are two major crises period which has severe impact to the world’s economics; they are 2008-2009 Subprime mortgage crises and 2012 Eurozone crisis, different with the Asian Financial crisis which rooted from Southeast Asia, those crises originated from advanced economies; however its impact also pronounced in this region. Related to the prior Asian Financial crisis Sufian and Habibullah (2010) concluded that this crisis negatively effect on bank’s profitability in Indonesia, further the profitability of Indonesian’s banks is relatively higher in the tranquil periods than in the crisis time. The effect of crisis to the bank’s performance could be different according to their owner, Cetoreli and Goldberg (2011) notes that foreign and local banks play different role in transmitting the shocks of the crisis, foreign banks abroad channel the shocks by reduce their cross border lending, foreign banks affiliations also decrease its local lending, and local banks follows reducing their loan because of interbank lending decline. The specific character of crisis effect on each different ownership status of banks could also affect their non-interest income generating performance.

The objectives of the study are first to analyze the ownership and the crisis impact to non-interest income, the non-interest income is classified and defined by Pennathur et al (2012) that group non-interest income into brokerage income and other non-interest income. We use different approach and based our classification on the model which is developed by De Young and Rice (2004) and also be used by De Young and Torna (2012) which categories non-interest income based on its production and risk return characteristics that could affect to insolvency and financial distress probability, further categorizing non-interest income into three kinds, namely non-traditional stakeholder activities, non-traditional fee for service and traditional fee. Non-traditional stakeholder activities are activities that require banks to hold risky asset i. e. , investment banking, venture capital and proprietary trading. Non-traditional fee for service activities are activities that don’t require banks to hold risky asset i. e. , securities brokerage and insurance sales. And then traditional fee activities are activities permitted prior to deregulation i. e. , fiduciary services and depositor services. However it’s difficult to classify the data that we have to those groups, so we still insist the category of non-traditional stakeholder activities, but uniting the non-traditional fee for service and traditional fee. This uniting makes sense because they don’t have substantial difference in the risk return characteristic; those non-interest incomes don’t require banks to hold risky asset in the generating process. After analyze the effect of ownership to non-interest income, we also will examine what factors could influence the non-interest income generating for each type of ownership. Specifically we will examine the effect of size, profitability, credit risk, lending business and capitalization to non-interest income generating in public, foreign and private banks. Second, we will investigate the effect


of recent crisis, which is originated from advanced economies. Although the deterioration effect of this crisis is not as big as Asian Financial crisis for Indonesian economics, but Indonesia banking sector must be affected significantly by this crisis. We attempt to assess the effect of this crisis to the Indonesian banks non-interest income generating activities. To support the assessment we also analyze the effect of crisis to non-interest income generating in each type of ownership of Indonesian banks.

The next section of this paper outlines the theory and related literatures.Section three present the data and methodology applied on this paper, while section four discuss the result and its analysis. Section five provide conclusion and will close the presentation of this paper.

II. THEORY

2.1. Selected Literature on Bank Ownership and Performance

The bank ownership cannot be separated from privatization issue currently, regarding to the impact of ownership, privatization and bank performance, Bonin et al (2005) investigated effect of bank privatization in European transition countries by computing income, balance sheet characteristic and efficiency. In terms of efficiency, they found that foreign owned banks are the most efficient on the other hand state banks are the least efficient. In fee for business service, found that local banks perform better because they have local advantage than foreign banks. Next, method and timing of privatization effect to the banks performance, voucher privatization doesn’t result in efficiency improvement, while early stage privatization resulted in better performance than the later one.

The ownership also related with the financing obstacles that faces by lender, Beck et al (2004) found that ownership structure of banking system coincide with level of economic development, regulatory and county’s characteristics affect relationship between financing obstacles and bank concentration. The relationship between financing obstacles and bank concentration dampens by the present of foreign banks along with high level of institutional development and efficient credit registry. On the other hand, greater restriction on bank activities, high intervention of government in banking system and higher share of banks in government ownership make the relationship of financing obstacles and bank concentration acerbated.

The comparison performance of public and private banks also presented by Iannotta et al (2007), after control bank characteristic, country and time effect found that mutual and government owned banks produce lower profitability than private banks even though they have lower cost. Government owned banks also have poorer quality of loan and higher insolvency risk than the other types of bank on the other hand mutual banks have better quality of loan and lower asset risk than other types of bank. Further, ownership concentration doesn’t affect profitability of banks; a higher concentration of ownership is resulted in better quality of loan and also asset and insolvency risk in lower level.


The more specific analysis of the relationship between ownership and income diversification is presented by Pennathur et al (2012) that examine the effect of ownership on income diversification and risk to Indian banks over the period 2001 to 2009. They found that noninterest income activities are affected by the ownership significantly. Furthermore public sector banks earn less fee income compared to private banks; on the other hand foreign banks generate higher fee income. Moreover public sector banks with higher share ownership of government tend to pursue non-interest income less intensively. On the relation with risk, fee based income reduces risk for instance default risk for public sector banks significantly.

2.2. Selected Literature on Bank’s Income Diversification

The relationship between size and technological advances to non-interest income was presented by De Young and Rice (2004) who found that non-interest income is generated relatively larger by large banks while well managed banks less depend on noninterest income. Moreover some technological advances such as mutual fund and cashless transaction are associated with noninterest income increases, while the other kind of technological advance such as loan securitization are linked with the noninterest income reduction. Further marginal increases of non-interest income are resulted in higher profit. The relation of risk return trade off are different in two periods, in the first part the risk-return trade off were improved by the expansion to non-interest income, but it worsened in the last part of observation period.

The effect of bank’s decision to either focuses or diversifies their activities to its return and risk is examined by Acharya et al (2006). Using data from 105 Italian firms over the sample period from 1993 to 1999, they found that there seems to be diseconomies of bank diversification particularly when it expands into industries that have higher degree of competition and without having prior experimentation in that area. Those diseconomies arise in the form of deteriorating credit quality of loan portfolios with a fall in bank returns.

The impact of bank activity and short term funding strategies for bank risk and return is analyzed by Demirguc-Kunt and Huizinga (2010), from 1. 334 banks in 101 countries, they concluded that the diversification to the non-interest income activities increases the rate of return of asset and it could offer risk diversification at very low level. Non-deposit funding in contrast lowers the rate of return of assets, but it offers risk reduction at low level. Further, banking strategies that rely on generating non-interest income or attracting non-deposit funding are very risky.

The research result from emerging countries observation also shows that non-interest activities increased bank’s risk. One of them proposed by Berger et al (2010) that examine the effect of product and geographical focus and diversification strategies on 88 Chinese banks during 1996-2006; they found dis-economies of diversification in the loan, deposit, asset and geographic dimension among those banks. Oppositely, more focused banks can attain higher profit and lower cost, as well as higher profit efficiency and higher cost efficiency.


2.3. Selected Literature on Income Diversification and Crisis

Whether income from non-traditional banking activities has a contribution to the failures commercial banks during financial crisis is investigated by De Young and Tokna (2013). They found that asset based non-traditional income increases the probability of bank failure especially for the banks that already suffered of financial crisis. The fee based non-traditional income has contrary effect, it reduces the probability that banks failed during crisis. This result confirmed that there is fundamental different production and characteristic of asset based non-traditional income and fee based non-traditional income.

The comparison of ownership effect on bank performance during crisis is explored by Cornett et al (2010), who found that from period of 1997 to 2000 in state banks the deterioration in cash flow returns, credit quality and capital core are greater than private banks. And then prior to 2001, in countries which government involvement and political corruption in banking is greater, state owned banks will have more inferior than private ones, some indicator of this inferiority performance is less profitability, less well capitalized and greater credit risk. However this gap can be closed by state banks in terms of cash flow returns, core capital and nonperforming loan in period of 2001 to 2004 or in the post-crisis period.

Relative similar study also conducted by Vallascas et al (2012) who examine the theory that states diversification will improve the resilience of banks during distress period. They found that banks which diversify their income in narrow level before the crisis experienced performance declining during financial crisis. Oppositely, broad diversification activities such as in lending and capital market activities prevent performance declining during the crisis.

Finally, the Indonesian banks profitability determinants during Asian financial crisis occurred is investigated by Sufian and Habibullah (2010). Form the data span from 1990 to 2005 they found that income diversification coincide with capitalization are positively associated to bank profitability, while overhead cost and size negatively impacted. Indonesian banks seem to have been skimping their resources especially during crisis and pre-crisis period. Moreover the Asian financial crisis exerts negative impact on Indonesian banks’ profitability, while in the tranquil period Indonesian bank were more profitable.

III. METHODOLOGY

3.1. Data

The data in this paper are obtained from Bankscope, the banking database that contain thousands of bank data around the world. We choose commercial banks which are owned by state, private national and private foreign banks as sample of this research. The definition of state banks here is the bank which is owned by the national state, not included bank which is owned by regional government. The definition of foreign bank is the bank which is owned


majority by foreign shareholder, so it would be both subsidiaries of foreign bank in Indonesia (for instance ANZ Indonesia Bank and HSBC Bank), and private Indonesian banks which is owned by foreigner (for instance CIMB Niaga and Danamon Bank). We select it by see the owner of that bank in the bankscope output sheet, and set the bankscope searching tool to find Indonesian banks which minimum 50. 01 % of its shares are belong to the foreign shareholder. The final sample comprises of 50 commercial banks, and the periods of the data are from 2005 to 2012.

Bankscope database provides the income statement of banks in certain format, dividing income in to interest income and noninterest income, for noninterest income there are six categories, they are net gain (losses) on trading and derivatives, net gain (losses) on other securities, net gain (losses) on assets at fair value through income statement, net insurance income, net fee and commissions and other operating income. We try to categories those incomes to be congruent with the taxonomy of non interest income which is developed by De Young and Rice (2004) which classifies non-interest income on to non-traditional stakeholder activities, non-traditional fee for service and traditional fee. Non-traditional stakeholder activities are activities that require banks to hold risky asset i. e. , investment banking, venture capital and proprietary trading. Non-traditional fee for service activities are activities that don’t require banks to hold risky asset i. e. , securities brokerage and insurance sales. And then traditional fee activities are activities permitted prior to deregulation i. e. , fiduciary services and depositor services. We match the categories from bankscope with the categorization of De Young and Rice (2004), there is no problem in defining the non-traditional stakeholder activities, in bankscope data they are net gain (losses) on trading and derivatives and net gain (losses) on other securities, those two categories have implication on bank to hold the risky asset, for the future this kind of income will be called “non-traditional stakeholder”. Then we continue to next categorization, the non-traditional fee for service and traditional fee activities, when we see the income data on bankscope, net insurance income and net fee and commission, one of them net insurance income could be fit with the non-traditional fee for service, but the other, net fee and commission could be on both non-traditional fee for service and traditional fee activities. To solve this problem, we unite these categories fee for service and traditional fee, we consider that apart for the difference between them, they have basic similarity, those non-interest incomes don’t require banks to hold risky asset to be generating. As the implication we also unite the net insurance income with the net fee and commission in the bankscope, for the future this income will be called “traditional and fee for service”.

3.2. Model

This paper attempts to investigate the effect of ownership and global crisis to the income diversification in Indonesian Banking. Further non-interest income will be divided on to two variables, they are non-traditional stakeholder and traditional and fee for service. To answer the research objectives, we use the model which has been developed by Pennathur et al (2012). For


the first objective to investigate the effect of ownership to income diversification, we deploy this model:

We employ two measures of non-interest income, first the ratio of non-traditional stakeholder income to total non-interest operating income and second traditional and fee for service income to total non-interest operating income. The ownership as independent variable measured by employ dummy variable for public bank and foreign bank as well. Control variables in this model are based mostly from Pennatur et al (2012), to capture the effect of size, we employ the log of total asset (LnTA), ROE is to capture the profitability difference, and the quality of loan is capture by Loan Loss Provision (LLP) controlled by total loan, the growth of business is captured by loan to Total Asset (L/TA) and to capture the capitalization effect, we employ capital to total asset (Cap/TA). Then we add the lagged of dependent variable (non-interest income at t-1) as the control variable, this variable will capture the effect of the last year non-interest income to this year non-interest income.

The other objective is to measure the effect of global crisis to the income diversification on Indonesian Banking. To capture the effect of global crisis we set a dummy on the year when global crises occurred. Our sample dataset period is 2005-2012, so we inventory the crises years upon that period. In determining whether that year could be classified as the crisis year, we consider based on the existing literature review. First, we conclude that the year of 2008 and 2009 is a crisis year, based on the systemic banking crisis database which has been updated by Laeven and Valencia (2012), they provide the database that include all systemic banking, currency and sovereign debt crises that span from 1970 to 2011. After reviewing that database, we conclude that the year of 2008 and 2009 can be classified the crisis year, because in those year occurs systemic banking crisis in the extensive scale in many countries, for instance in 2008 there were banking crises in Austria, Belgium, Iceland, Latvia, Luxembourg, Netherland, United Kingdom and United States, In 2009 systemic banking crisis occurred in Denmark, Germany, Greece, Ireland, Mongolia and Ukraine. The other crisis year that we noted is in the year of 2012, we draw this conclusion based on Aizenman et al (2013) that stated in the year of 2012 the Euro zone sovereign debt crisis posed ad become the single biggest downside risk to the global outlook. We also see the record of world growth rate from the databank World Bank

(1)


database in our sampling period and find that the lowest growth rate record are occurred in the year 2008, 2009 and 2012.

Then, we would like to investigate the determinant of non-interest income and the effect of crisis to each ownership type, so we will have panel data regression for state bank, foreign bank and private bank respectively. This specific analysis will provide the more comprehensive understanding and enrich the result of the previous analysis. Still the same with previous model, the dependent variables for this analysis are non-traditional stakeholder and traditional and fee for service. The model then written as the equation bellow:

(2)


4.1. Ownership and Income Diversification

The analysis begins with examining the impact of ownership to income diversification, further it would examine whether there are differences in non-interest income both in non-traditional stakeholder income and in traditional and fee for service income. Based on the descriptive statistic, the mean value of non-traditional stakeholder income to total non-interest operating income for all samples is 16. 08 percent, which is lower than the traditional and fee for service income to total non-interest operating income which is 46. 25 percent. This result reveals that traditional and fee for service non-interest income is much common non-interest income for Indonesian Banks. The complete result of descriptive statistic and correlation matrix is provided in Table 1.

We continue in examining the effect of ownership on the income diversification. In estimating the effect of ownership and global crisis to income diversification for all banks we use pooled effect panel data, we choose that method with regard to two considerations; first, because the ownership ofbanks doesn’t change over time (dummy of public and foreign banks will be the same every year) so it can’t be estimated by fixed effect, second, random effect cannot be used either as can be seen on the value of hausman test (the p value is significant for all of models). In addition, we augment the year dummy to the model. The usage of pooled effect panel data to calculate income diversification also be used by Pennathur et al (2012). The result of the estimation is provided in Table 2.


Independent variables in this model seem to be able to explain traditional and fee for service better than non-traditional stakeholder. It can be seen from the value of R2, where its value is bigger in dependent variable of traditional and fee for service (38. 57% and 39. 72% in model 2 and model 4) than in dependent variable of non-traditional stakeholder (20. 58% and 21. 70% in model 1 and model 3). The lagged of dependent variable performs effectively capture the effect of last year non-interest income, for all models the lagged of dependent variable is significantly affect dependent variable at the level of 1 %. This variable also prevent the presence of first order serial correlation, it is proved by the value of Durbin-Watson statistic which is near two, indicating no first order serial correlation. Regarding to the possibility of multicollinearity between public and foreign ownership, we test its presence by running models one without public ownership variable and the other without foreign ownership variable interchangeably and finding that the value of R2 doesn’t change, it indicates that there is no multicollinearity between public and foreign ownership.

Model 1 and model 2 in Table 2 reveal that ownership has no explanation power as the determinant of the non-traditional stakeholder non-interest income, the p value of public and foreign ownership in both non-traditional stakeholder and traditional and fee for service is not significant. However, the direction effect of public ownership is negative in both non-traditional incomes; in contrast the direction of foreign ownership is positive. Even though not significant, but the direction of public and foreign ownership to income diversification confirms previous arguments that public banks performance is poorer, and foreign banks has a better performance (Bonin et al, 2005; Iannotta et al, 2007; Pennathur et al, 2012).

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For control variables, the variable that affect non-traditional stakeholder and traditional and fee for service is different. For non-traditional stakeholder, capitalization (capital to total asset) significantly affect non-traditional stakeholder with positive direction. It means that more well capitalized the bank more generating non-traditional stakeholder non-interest income. This positive effect of capitalization is in line with argument of Blum (1999) about the “leverage effect” of capital rules. It stated that banks will maximize the value of its equity by invest it in the profitable business although it is more risky. On the other hand profitability (ROE) has a positive and significant effect to traditional and fee for service non-interest income. From the coefficient value, reflected that one percent increase of ROE leads to 0. 13% increase in traditional and fee for service non-interest income. This result is contrary to De Young and Rice (2004) which found that the profitability affect negatively to the non-interest income.

Table 3 provides the estimation result of some determinants of non-interest income for each of ownership type. We analyze this estimation use pooled effect panel data, we decide it based on two considerations, first, because the number of observation is small (the smallest number of observation is 30 and the largest is 145) so it is impossible to use fixed effect because it can raise small sample bias (Nickell, 1981), second we consider to use random effect panel data, but it cannot be done because of the value of Hausman test (p value is significant in all calculation).

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We then continue the estimation of each type of ownership. For public bank capitalization (capital to total asset) positively and significantly affect non-traditional stakeholder non-interest income. This is in line with the estimation of non-traditional stakeholder for all of bank’s ownership categories. In terms of traditional and fee for service, in state banks, size (ln of asset) has a positive and significant effect. It means that the bigger the state bank, the greater the earnings of traditional and fee for service. This finding is in line with Hidayat et al (2012) who also state that the income diversification as the result of deregulation in Indonesia is done by big banks, because it has significant role in Indonesian banking industry.

For foreign banks, we found that none of the independent variable has significant effect to non-traditional stakeholder non-interest income unless lagged of dependent variable. However, for traditional and fee for service non-interest income credit risk (loan loss provision to loan ratio) has a positive and significant effect. The reason which could explain this result is possibly banks attempt to seek another income because the main income from lending activities faces quite significant risk. This finding is different with Pennathur et al (2012) which found a positive and significant effect of credit risk effect on fee based income not in foreign but private banks.

For private banks, similar to foreign banks, none of the independent variable has significant effect to non-traditional stakeholder non-interest income unless lagged of dependent variable. In terms of traditional and fee for service there are two variables which has significant effect.


First, profitability (ROE) which has positive value, this result in line with estimation of traditional and fee for service for all bank’s ownership categories. From the coefficient value can be interpreted that one percent increase of ROE leads to 0. 25% increase of traditional and fee for service non-interest income. Second, credit risk (loan loss provision to loan ratio) which has negative value, and also the coefficient value is quite big, one percent increase in LLP to loan ratio will decrease traditional and fee for service 0. 54%. This result may indicate that main interest income activities and traditional and fee for service non-interest income activities run together in private banks. Another variables that related to main interest income activities like lending business (loan to total asset) also has positive value although not significant. This finding provides a more comprehensive understanding, that the role of credit risk to traditional and fee for service non-interest income in foreign and private banks is different. It has positive effect in foreign banks but affect oppositely in private banks.

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4.2. Global Crisis and Income Diversification

We then examine the effect of global crisis to income diversification, the result of panel data estimation is exhibited in Table 2, in detail model 3 provides the effect of global crisis to non-traditional stakeholder non-interest income, while model 4 provides the effect to traditional and fee for service non-interest income. There are two different results, for non-traditional stakeholder, the effect of crisis is negative but it’s not significant, on the other hand, it has significant and positive effect to the traditional and fee for service. The negative effect to non-traditional stakeholder non-interest income could be caused by shocks in capital market and other financial markets that imply to the financial asset pricing decreases, As the non-traditional stakeholder is the non-interest income that comes from risky activities such as from investment banking, venture capital and proprietary trading that highly depend on the asset price in financial markets. Longstaff (2010) finds strong evidence the contagion of subprime


crisis to the other financial market, further by investigating the pricing of subprime asset backed collateralized debt obligations (CDOs), he finds that financial contagion to other markets is propagated through liquidity and risk premium channel. The positive effect of crisis to traditional and fee for service reveals that during global crisis, the pressure of crisis that disturbs banks performance is later compensated by intensify the other source of income, the traditional and fee for service non-interest income.

Then we investigate the effect of global crisis for each of ownership type, the estimation result for this analysis is exhibited in Table 4. We calculate this estimation by using pooled effect panel data with similar reasons with previous session in calculating Table 3. For the effect to non-traditional stakeholder non-interest income, we find that the global crisis has negative effect on foreign and private banks but not significant. However it has positive effect on state banks although it’s not significant either. The different direction effect of global crisis to private and foreign banks in one side and public bank in other side could be caused by different source of non-traditional stakeholder non-interest income. As mentioned before, this kind of non-interest income sourced from holding risky assets, perhaps assets which are held by private and foreign banks more exposed by global crisis (probably hold more international assets which its value fragile of global crisis), while state banks hold different type of asset.

Lastly, the effect of global crisis to traditional and fee for service non-interest income is significantly occurred with the positive direction in foreign banks. It means that during the crisis traditional and fee for service non-interest income of foreign banks increased. This finding in line with Jeon and Miller (2005) that found a steady performance of foreign banks in Korea during Asian Financial crisis while it deteriorated domestic banks performance, one of the reasons for this is because foreign banks in Korea rely more on fee for service income than from lending interest income. Foreign banks in Indonesia seems to follow similar pattern, Hadad et al (2004) concluded that foreign banks in Indonesia were more focus on fee based income business but less active in its intermediation function. So the positive performance of foreign banks to generate traditional and fee for service during global crisis could be caused by the nature of foreign banks which more specialized in non-interest income.


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365The Effect of Ownership and Global Crisis to Income Diversification of Indonesian Banking��

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V. CONCLUSION

Indonesian banking is growing rapidly and attracts diverse investors to participate in this business sector. Several parties which become significant shareholder of Indonesian banking are government, private and foreign owner. The turbulence of world economic raises financial crises which potentially harm the progress of Indonesian banking growth. This paper attempts to examine the effect of ownership and global crisis to income diversification of Indonesian Banks during period of 2005 to 2012. The income diversification is classified as the taxonomy of De Young and Rice (2004), in this paper we divide the income diversification to the non-traditional stakeholder non-interest income and traditional and fee for service non-interest income.

Our result suggests that ownership doesn’t affects income diversification of Indonesian banks both to the non-traditional stakeholder and traditional and fee for service non-interest income. However, there is a difference in terms of direction, the direction effect of public ownership is negative in both non-traditional incomes, and on the other hand the direction of foreign ownership is positive. Based on the direction of the ownership effect, this result support Pennathur et al (2012) that public banks do not intensively generate their non-interest income, and on the other hand foreign banks can maximize this source of income better. Then, when analyzing in each types of ownership, for public bank capitalization affect significantly in positive direction to non-traditional stakeholder non-interest income. In terms of traditional and fee for service, in state banks, size has a positive and significant effect. For foreign and private banks none of the variables affect non-traditional stakeholder non-interest income unless lagged of


dependent variable. In Traditional and fee for service non-interest income, in foreign banks credit risk significantly affect in positive direction, but in it affect oppositely in private banks. In addition profitability also affects significantly and positively to traditional and fee for service non-interest income in private banks.

The effect of global crisis has different direction to each non-interest income, for non-traditional stakeholder non-interest income it is not significant and has negative effect. However it significantly encourages banks to generate traditional and fee for service non-interest income, this finding reveals that the decline in interest based income due to the crisis condition push banks to compensate through maximize traditional and fee for service non-interest income. For each ownership type’s investigation, we find no evidence that global crisis affect non-traditional stakeholder non-interest income generating for all types of ownership, public, private and foreign banks. In terms of traditional and fee for service non-interest income, global crisis has significant and positive effect to traditional and fee for service generating in foreign banks. This finding support Jeon et al (2005) that found better performance of foreign banks than domestic banks during the Asian financial crisis because they rely more on fee for service.

These findings have several implications, first the taxonomy of De Young and Rice (2004) should be recognized well, because different kind of income diversification has different characteristics, so everyone should avoid generalizing income diversification. Second, the determinant factors that affect income diversification for each type of ownership are not similar. It should be realized that the effort to maximize non-interest income would be different. Third, related to the bank supervision during crisis period, regulator should concern about different effect of crisis to non-interest income generating by different types of banks, so necessary policies could be taken properly.


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