Capital inflows and asset prices: Evidence from emerging Asia

Journal of Banking & Finance 37 (2013) 717–729

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Journal of Banking & Finance

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Capital inflows and asset prices: Evidence from emerging Asia

Peter TillmannDepartment of Economics, Justus-Liebig-University Giessen, Licher Str. 66, 35394 Giessen, Germany

a r t i c l e i n f o a b s t r a c t

Article history:Received 1 March 2012Accepted 10 October 2012Available online 8 November 2012

JEL classification:F32F41E32

Keywords:Capital inflowsHouse pricesMonetary policySign restrictionsPanel VAR

0378-4266/$ - see front matter � 2012 Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.jbankfin.2012.10.017

E-mail address: [email protected] The numbers are taken from IMF (2011a). See Til

flows to Asia during the crisis and IMF (2011b), Ostryet al. (2012) for discussions of policy responses.

The withdrawal of foreign capital from emerging countries at the height of the recent financial crisis andits quick return sparked a debate about the impact of capital flow surges on asset markets. This paperaddresses the response of property prices to an inflow of foreign capital. For that purpose we estimatea panel VAR on a set of Asian emerging market economies, for which the waves of inflows were partic-ularly pronounced, and identify capital inflow shocks based on sign restrictions. Our results suggest thatcapital inflow shocks have a significant effect on the appreciation of house prices and equity prices.Capital inflow shocks account for – roughly – twice the portion of overall house price changes theyexplain in OECD countries. We also address cross-country differences in the house price responses toshocks, which are most likely due to differences in the monetary policy response to capital inflows.

� 2012 Elsevier B.V. All rights reserved.

1. Introduction

Over the recent years emerging market economies experiencedlarge swings in net capital inflows. While net capital inflowspeaked in early 2008 at about 4% of emerging markets’ GDP, theydropped to �2.5% following the collapse of Lehman Brothers atthe height of the financial crisis. Interestingly, however, capitalflows quickly resumed in early 2009. In Asia, flows already ex-ceeded the pre-crisis level in early 2010.1

Capital inflows are, in principle, highly welcome in emergingeconomies. They lower the costs of funding, help raise the standardof living and thus facilitate convergence with advanced economies.Likewise, cross-border flows, by offering investment opportunitiesand extending the set of available assets, contribute to economicefficiency and risk sharing also in the source countries. Neverthe-less, capital inflows often have many unwarranted effects: First,they can lead to a real exchange rate appreciation that underminescompetitiveness in the tradeable goods sector. Second, by prevent-ing the central bank from tightening monetary policy, they canlead the economy to overheat, generating inflationary pressures.Third, they can trigger and prolong asset price bubbles and amplifyfinancial fragility.

ll rights reserved.

sen.dele (2011) a survey on capitalet al. (2011) and Balakrishnan

The latter impact is the focus of this paper. In light of the recentfinancial crisis that originated in a housing price bubble in the US,researchers and policymakers focus again on the housing market asa key indicator for financial imbalances and macroeconomic risks.Federal Reserve chairman Bernanke (2010) explicitly linked capitalinflows to accelerating house price inflation and bubbly propertyprices. Although he focused on the US case, the capital flow-houseprice nexus is arguably even more important for emergingcountries.

This paper studies the response of property prices in emergingmarket economies to an inflow of foreign capital. Our contributionis threefold:

First, we estimate a panel vector autoregression (VAR) on a setof Asian emerging market economies for which the waves of in-flows were particularly pronounced. A panel approach is best sui-ted to summarize the data in light of the short sample periodavailable after the disruptions of the Asian financial crisis. The pa-per focuses on Asia because capital quickly returned to this regionafter the 2008 financial crisis, inflows are more homogenous acrosscountries in this region than compared to, say, Latin America and,finally, house prices experienced considerable upward pressureover the past years.

Second, we use sign restrictions following the work of Uhlig(2005) and, in particular, Sá et al. (2011) to identify capital inflowshocks and the responses of house prices and equity prices to theseshocks. Our approach avoids an arbitrary ordering of the variablesthat often characterizes triangular identification schemes used in

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Table 1VAR specifications.

VAR I VAR II

Sample 2000:1–2011:1 2003:1–2010:4Lag order 4 3Countries HKG, KOR, MAL, THA, TWN HKG, KOR, MAL, SGP, THA, TWNVariables FLOWSit, GDPit, Pit, REERit,

ASSETit, LONGit, SHORTit

Table 2Sign restrictions.

Restriction on VAR with total capitalinflows

VAR with portfolioinflows

Sign Horizon Sign Horizon

Capital inflows + K = 2 + K = 1GDP + K = 2 + K = 1Price level Unrestricted UnrestrictedREER appreciation + K = 2 + K = 1Asset prices Unrestricted UnrestrictedLong rate � K = 2 � K = 1Short rate Unrestricted Unrestricted

4 In a recent theoretical contribution, Adam et al. (2012) develop an open economy

718 P. Tillmann / Journal of Banking & Finance 37 (2013) 717–729

other VAR studies on asset price dynamics and monetary policy re-viewed below. The shock we identify can best be interpreted as anunexpected increase in foreigner’s demand for domestic assets. Thedriving forces behind international capital flows are often classifiedin terms of push and pull factors. Push factors, defined as financialand macroeconomic conditions in advanced economies, lead inves-tors in advanced economies to send funds to emerging markets. Incontrast, pull factors are given by conditions in the recipient coun-tries attracting foreign investors. The capital inflow shock identi-fied here is consistent with a shock to push factors.2

Third, we use the estimated panel VAR to shed light on cross-country differences in the responses of both types of asset prices,i.e. house prices and equity prices, to capital inflow shocks. For thatpurpose we exclude each country in turn from our panel VAR andestimate the VAR on the remaining set of countries. This gives us aset of impulse response functions from which the relative effectstemming from one country in the panel can be gauged.

Our results suggest that capital inflow shocks had a significanteffect on real house price appreciation. A shock that increases netcapital inflows relative to GDP by one percentage point leads toan increase in real house prices of 0.5%. Although capital inflowshocks account for only a moderate small portion of overall houseprice changes, about 10–15% depending on the specification, thisfraction is about twice as large as what has been found for OECDcountries.3 The shocks we identify capture the capital flight in2008 and the massive return of capital coinciding with the uncon-ventional monetary policies in industrial countries since 2009. Tocorroborate these findings, we also estimate the responses of equityprices to capital inflow shocks and restrict capital flows to portfolioinflows only. Finally, we find important cross-country differences inthe sensitivity to capital flow shocks, which cannot be explained bymortgage market characteristics or property market regulation. In-stead, our results are consistent with the view that aggregate macropolicies such as the monetary policy response to inflows are themain determinant of cross-country heterogeneity.

The remainder of the paper is organized as follows. The follow-ing section briefly summarizes the related literature. Section 3

2 Milesi-Ferretti and Tille (2011) and Forbes and Warnock (2011) stress the role ofpush-factors for recent periods of massive capital inflows. See also Förster et al.(2012) for an analysis of the global comovement of capital flows.

3 See Sá et al. (2011) for these findings for OECD countries.

introduces the panel VAR model, provides details on the data set,the construction of the main variables and explains the identifyingrestrictions. The main findings are discussed in Section 4. Section 5presents results from alternative specifications to corroborate therobustness of the previous findings. Section 6 sheds light on thecross-country heterogeneity in the asset price responses to capitalinflow shocks. Section 7 summarizes the results and draws someconclusions.

2. Related literature

The present paper contributes to understanding the linkagesbetween capital inflows and asset price surges with a particular fo-cus on house price dynamics. Three strands of the literature areparticularly relevant for this task. We briefly portray some key con-tributions to each strand with an eye on VAR studies and pay par-ticular attention to papers addressing Asian economies.

First, recent papers estimate reduced form relationships be-tween asset prices and the current account.4 Based on a largecross-section of countries Kole and Martin (2009) find a robust neg-ative correlation between the growth rate of house prices and thechange in a country’s current account balance. Likewise, Aizenmanand Jinjarak (2009) find a strong positive relationship between cur-rent account deficits and real estate prices. The causality betweenhouse prices and the current account is studied by Jinjarak andSheffrin (2011). They argue that current account deficits were unli-kely to directly drove real estate prices in the US, Spain and Ireland.As shown by Kannan et al. (2011), after 1985 a deteriorating currentaccount balance is shown to be a strong leading indicator for houseprice busts in OECD countries.

Second, some studies use VARs to estimate the dynamic inter-action between asset price, capital flows and the macroeconomyand explicitly identify capital inflow shocks. Kim and Yang(2009) use a VAR model to analyze the effects of capital inflowshocks on asset prices in Korea. They find that capital inflowshocks have an effect on equity prices but not on property prices.These shocks are, however, identified by imposing a recursiveordering onto the variables. In light of the mutual interactions be-tween asset prices, capital flows and the macroeconomic environ-ment imposing this ordering requires a substantial amount onarbitrariness. Think of the relationship between asset price andmonetary policy shocks. A triangular identification scheme forcesthe researcher to impose ex ante the direction of causality be-tween asset prices and monetary policy within a quarter. In a re-lated paper, Kim and Yang (2011) extend their work to a panelVAR estimated on five Asian economies between 1999 and 2006.Again, capital inflow shocks explain only a small fraction of assetprice fluctuations. This paper suffers from the same weakness asthe authors rely on an ad-hoc ordering of the variables to interpretthe estimated shocks.

The relationship among asset markets and the current accountis also studied by Fratzscher et al. (2010), although with a slightlydifferent focus. The authors use a VAR with a sign-restriction iden-tification scheme to assess the impact of asset market shocks onthe US current account. While a few studies try to identify capitalflows shocks, Helbling et al. (2011) instead use sign restrictions toidentify a credit shock. According to their estimates, credit shocks,in particular those originating in the US during the recent global

asset pricing model for the G7 economies in which households entertain subjectivebeliefs about price behavior that are potentially decoupled from fundamentals. A two-country two-sector model which illustrates the link between a property price boomand the current account is presented by Punzi (2012). Favilukis et al. (2011) arguethat capital flows play only a limited role in boom-bust cycles in property prices.Instead, they point to the reversal of financial market liberalization as a key driver.

Response of capital inflows

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Fig. 1. Impulse responses after a capital inflow shock obtained from a five-country VAR with total capital inflows and house prices.


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Fig. 2. Impulse responses after a capital inflow shock obtained from a five-country VAR with total capital inflows and equity prices.

P. Tillmann / Journal of Banking & Finance 37 (2013) 717–729 719


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Fig. 3. Impulse responses after a capital inflow shock obtained from a five-country VAR with portfolio inflows and house prices.


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Fig. 4. Impulse responses after a capital inflow shock obtained from a five-country VAR with portfolio inflows and equity prices.



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Fig. 5. Impulse responses after a capital inflow shock obtained from a six-country VAR with total capital inflows and house prices.


financial crisis, are an important driver of fluctuations in the G7economies.

The study by Sá et al. (2011) is closest to our paper and esti-mates a panel VAR for OECD countries. Capital inflow shocks de-rived from sign restrictions are shown to be important drivingforces of house prices and other housing market variables. More-over, the large panel dimension allows the authors to revealcross-country differences and the relation to mortgage marketcharacteristics. A better developed mortgage market leads to evenstronger effects of capital inflow shocks. In the empirical analysisbelow we apply a similar identification scheme to a panel of Asianemerging market economies.

Third, recent studies focus on the response of property marketsto monetary policy shocks. Assenmacher-Wesche and Gerlach(2008) and Goodhart and Hofmann (2008) estimate panel VARson 17 OECD countries to show that monetary policy shocks havea significant effect on asset prices. A VAR for several Asian emerg-ing economies is estimated by Bracke and Fidora (2008). Theauthors use sign restrictions to identify monetary policy shockswhich are shown to explain a large part of asset price fluctuations.To improve the efficacy of the estimation, the authors aggregateindividual economies using GDP weights.

Vargas-Silva (2008) uses sign restrictions to quantify the re-sponse of US house prices to monetary policy shocks. He finds thathousing starts and residential investment responds to a policytightening, although the impact is affected by a large degree ofuncertainty. Similarly, Mallick and Sousa (2012) provide evidenceon the effects monetary policy shocks, again identified via signrestrictions, on real equity prices in large open economies suchas Brazil, China, Russia and South Africa. Our method is also similarto the recent work of Carstensen et al. (2009) and Hristov et al.

(2012), who estimate a panel VAR with shocks identified via signrestrictions. They are, however, interested in identifying loan sup-ply shocks and monetary policy shocks, not capital inflow shocks.

3. The empirical model

3.1. A panel VAR approach

A panel structure allows us increase the degrees of freedom inthe estimation process in light of the short sample period afterthe Asian crisis of 1997. This will lead to more efficient estimatesthan a country-by-country analysis. The estimated panel VAR oforder l takes the following form

Yit ¼ Bi0 þ B1Yit�1 þ B2Yit�2 þ � � � þ BlYt�l þ uit ð1Þ

with time index t = 1 � l, . . . ,T and country index i = 1, . . . ,N whereYit is an m � 1 vector of data for country i, Bj are m �m coefficientmatrices and uit is the vector of one-step ahead prediction errorswith variance–covariance matrix R. The vector Bi0 consists of coun-try-specific intercepts. We collect the coefficient matrices inB ¼ B01; . . . ;B0l

� �. The matrix polynomial in the lag operator L is B(L).

In order to translate the reduced-form innovations uit intomeaningful structural shocks vit, we need a matrix A such that

uit ¼ Av it ð2Þ

with R ¼ E uitu0it� �

¼ AE v itv 0it� �

A0 ¼ AA0. There are m(m � 1)/2 degreesof freedom in specifying A. The identifying restrictions needed to ob-tain that, besides those emerging from the covariance structure, areimposed following Uhlig’s (2005) seminal (pure) sign-restrictionsapproach. One popular alternative would to let A be a Cholesky


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Fig. 6. Impulse responses after a capital inflow shock obtained from a six-country VAR with total capital inflows and equity prices.


factor of R, which implies a recursive – but often arbitrary – orderingof the variables.

The identification is achieved by imposing restrictions on thesign of the impulse responses of the endogenous variables.5 Uhlig(2005) shows that any impulse vector acan be recovered if anm-dimensional vector q of unit length is chosen such that a ¼ eAq,where eAeA0 ¼ R, and eA is the lower triangular Cholesky factor of R.6

A Normal-Wishart prior in (B(L),R) is formed for the reduced-form VAR. The posterior is the Normal-Wishart for (B(L),R) timesthe indicator function that discriminates the draws on the basisof the imposed sign restrictions. For each q draw, we computethe associated a vector and calculate the impulse responses as de-scribed before. If the resulting impulse response has the correctsign, i.e. the prespecified sign, the draw is kept. If not, the drawis discarded. We take n1 draws from the VAR posterior and n2

draws from an independent uniform prior. The impulse responsesare calculated at horizon k = 0, . . . ,K (in quarters). We stop afterobtaining n3 impulse response functions with the desired sign.The error bands are calculated using the draws kept. We setn1 = n2 = 2000 and n3 = 1000.

The model is estimated by Ordinary Least Squares (OLS). Thiswarrants some discussion as the OLS estimator with fixed-effectsis known to be potentially biased in a dynamic panel setting ifthe coefficients on the endogenous variables differ across coun-tries. As discussed in Assenmacher-Wesche and Gerlach (2008),restricting the coefficients to be the same across groups inducesserial correlation in the residuals when the regressors are

5 The following exposition follows Uhlig (2005) and Fratzscher et al. (2010).6 An alternative identifiction based on a combination of short and long run

restrictions is proposed by Bjornland and Jacobsen (2010).

autocorrelated. A popular way to solve this problem is to applyPesaran and Smith’s (1995) mean-group estimator, which is usedin Assenmacher-Wesche and Gerlach (2008) and Sá et al. (2011).To be a viable solution, however, the mean-group estimator re-quires the time dimension of the panel to be sufficiently large toestimate country-specific VARs whose coefficients can be averagedacross countries. This requirement is clearly violated in our setupas the sample period covers one decade only. In fact, Rebucci(2003) finds that the mean-group estimator typically requires atime dimension that exceeds the length of a typical macroeco-nomic dataset. To address the issue of cross-country differences,besides the illustrative approach presented below, we also esti-mated the model using a random-coefficient approach in whichthe coefficients in the Bj matrices are allowed to vary randomlyaround a common mean. This yields results that are virtually iden-tical to those obtained with the fixed-effects estimator. In light ofthese difficulties, we therefore continue to use the fixed-effectsestimator.

3.2. The data set

We estimate the model for Asian emerging economies usingtwo alternative VAR specifications, each of which is estimated fortwo alternative capital inflow series and two alternative assetprices. Our estimation period and the set of countries is dictatedby data availability. For many Asian economies, reliable houseprice indices are available only after the Asian crisis. This leavesus with five economies, for which data is available from 2000:1,i.e. Korea (KOR), Hong Kong (HKG), Malaysia (MAL), Thailand(THA) and Taiwan (TWN). The estimation period for this sampleends in 2011:1. Based on standard lag selection criteria the lag


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Fig. 7. Impulse responses after a capital inflow shock obtained from a six-country VAR with portfolio inflows and house prices.

7 Kim and Kim (2011) study the role of capital inflows for the degree of businesscycle synchronization in Asia and find an expansionary effect of capital inflow shockson GDP.


order for this panel VAR, henceforth referred to as VAR I, is set tol = 4. For Singapore house prices are available from 2003. Therefore,we set up a second VAR, henceforth VAR II, which also covers Sin-gapore (SGP). The lag order of this VAR model, for which the esti-mation sample ends in 2010:4, is set to l = 3. Table 1 summarizesthe alternative VAR specifications.

Each VAR contains the following quarterly data series: net cap-ital inflows in percent of GDP (FLOWSit), log real GDP (GDPit), thelog consumer price index (Pit), the log real effective exchange rate(REERit), a log real asset price (ASSETit), the long-term (LONGit) andthe short-term interest rate (SHORTit). All variables enter the VARin levels. Thus, the vector Yit consists of

Yit ¼ ½FLOWSit GDPit Pit REERit ASSETit LONGit SHORTit�0 ð3Þ

To assess the role of different types of capital inflows, the FLOWSit

variable represents either total net capital inflows defined as thesum of foreign direct investment, portfolio and other types of cap-ital inflows or portfolio capital inflows only. We are also interestedin contrasting the response of house prices to capital inflow shockswith that of other equity prices. For that purpose, the ASSETit vari-able represents either real house prices or real equity prices. A high-er value of REERit means a real appreciation of the domesticexchange rate.

The macroeconomic data series are taken from the IMF’sInternational Financial Statistics Database, while the real effectiveexchange rate series are obtained from the BIS’s website. Data onhouse prices is taken from the CEIC database.

3.3. The identifying restrictions

In this paper a capital inflow shock is interpreted as anunexpected inflow of foreign capital unrelated to domestic

fundamentals, thus resulting from global push-factors. This re-quires us to carefully distinguish a capital inflow shock from othersources of capital inflows, e.g. a shock to domestic productivity ordemand, which would also attract foreign inflows of capital.

The set of restrictions imposed in this paper is summarized inTable 2.

These restrictions correspond to those imposed by Sá et al.(2011) extended by a constraint on the output response. An expan-sionary capital inflow shock is supposed to increase capital inflows,leads to an increase in economic activity, puts appreciation pres-sure on the real effective exchange rate and lowers long term inter-est rates. The restrictions are imposed for a horizon of K = 2quarters for the VAR model containing total capital inflows andK = 1 quarter for the portfolio-VAR. Given the volatility of net port-folio inflows, a restriction over two quarters might be toorestrictive.

These effects are consistent with a broad range of empiricalstudies on capital inflows to emerging economies. Let us brieflysketch selected recent contributions. A complete survey is beyondthe scope of this paper: Cardarelli et al. (2010) analyze the implica-tions of surges in private capital inflows in a large group of emerg-ing and advanced economies. They conclude that capital inflowperiods are associated with an acceleration of GDP growth and areal appreciation.7 Likewise, Jongwanich (2010) estimates a dy-namic panel model to understand the nexus between capital flowsand real exchange rates for the period 2000-2009, which roughlycorresponds to our sample. He shows that both portfolio and FDI in-flows lead to a significant real appreciation.


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Fig. 8. Impulse responses after a capital inflow shock obtained from a six-country VAR with portfolio inflows and equity prices.


Capital inflows also increase liquidity and depress long-terminterest rates. In the present setting, this constitutes an unwar-ranted monetary easing as many economies operate close to oreven above potential and face strong inflationary pressure. The re-cent study by Pradhan et al. (2011) finds that an increase in non-resident participation in local bond markets by one percentagepoint reduces nominal long-term bond yields by about five basispoints on average. As discussed in Sá et al. (2011), restricting thelong-term interest rate is crucial in order to distinguish a capitalinflow shock, i.e. an unexpected increase in foreign demand fordomestic assets, from other shocks. A positive productivity shockor a demand shock, for example, would also result in capital in-flows and a real appreciation, but would increase rather then de-crease the (real) long-term interest rate. Since Sá et al. (2011)find that including the nominal or the real interest rate makes nodifference for the results, we opt for the nominal interest rate forreasons of data availability.

Note that the response of the asset price, which is the central fo-cus of this paper, is left unrestricted. The same is true for the mon-etary policy response, i.e. the short-term interest rate, and the pricelevel.

4. Results

The resulting impulse response functions for both models, i.e.the VAR I and VAR II, two alternative asset price series, i.e. houseprices and equity prices, and two alternative capital inflow seriesare reported in Figs. 1–8. All figures show the response of the sevenendogenous variables to a capital inflow shock one standarddeviation in size. In all figures the solid line represents the median

response across all draws. The surrounding confidence bands areconstructed using the 16th and 84th percentile of the acceptedresponses.

The core result is that a positive capital inflow shock leads to asignificant and persistent appreciation of both house prices andequity prices. A shock of one standard deviation leads to capital in-flows of 1% of GDP and an increase in real house prices of about0.5%, see Fig. 1. The resulting increase in equity prices is almosttwo times as large, see Fig. 2. The impulse responses also revealthat consumer prices increase for a prolonged time period afteran unexpected surge in capital inflows. Monetary policy tightenswith some time lag of about three to four quarters. The VARs esti-mated with portfolio inflows instead of total capital inflows, seeFigs. 3 and 4, yield very similar results. The six-country VAR spec-ification, our VAR II model, generates slightly stronger house priceresponses than the five-country model, see Fig. 5, when estimatedon total capital inflows. This reflects the house price surge in Sin-gapore, which is missing in the smaller VAR I model. The equityprice response, see Fig. 6, is insignificant. Likewise, for portfolio in-flows, see Figs. 7 and 8, the response of the asset price becomesinsignificant. Another striking finding from the VAR II is the nega-tive response of the short-term interest rate following a shock tocapital inflows. This finding is also discussed in the followingsection.

Table 3 reports the fraction of the forecast error variance of as-set prices explained by the capital inflow shock. Two findings standout. First, the explained portion is larger when the model is esti-mated on portfolio flows only rather than on total inflows. Second,when compared to the findings of Sá et al. (2011), the fraction ofhouse price variance explained by capital inflow shocks is roughlytwice as large as in OECD countries. Although the overall effect

Table 3Forecast error variance decomposition.

Model Forecast horizon(in quarters)

Variance share explained bycapital inflow shock

With totalcapital inflows

With portfolioinflows

VAR I 1 0.09 0.14With house prices 4 0.08 0.15

8 0.08 0.1512 0.10 0.14

VAR I 1 0.15 0.12With equity prices 4 0.14 0.13

8 0.15 0.1312 0.15 0.13

VAR II 1 0.11 0.15With house prices 4 0.10 0.13

8 0.11 0.1212 0.11 0.11

VAR II 1 0.12 0.14With equity prices 4 0.11 0.14

8 0.12 0.1312 0.12 0.13

2007 2008 2009 2010-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

Fig. 10. Capital inflow shocks obtained from a five-country VAR with total capitalinflows and equity price.

-1.00

0.00

1.00

2.00


appears moderate with a share of about 10–15% of house pricedynamics accounted for by capital inflow shocks, two remarksare warranted. First, these numbers refer to real house prices.Given the persistent inflationary effect of capital inflows shocks,the eventual response of nominal house prices will be much larger.Second, the capital flow shocks reflect changes in global push-factors only. In addition to that, capital flows attracted by domesticconditions such as favorable growth prospects, would also contrib-ute to asset price developments.

Figs. 9–12 depict the identified capital inflow shocks for the re-cent crisis period, i.e. from the first quarter of 2007 to the last quar-ter of 2010. The shocks capture the boom-bust cycle observed inaggregate capital inflow data. Large negative shocks are observedfor the second half of 2008, i.e. after the Lehman collapse, followedby exceptionally strong inflows in the first half of 2009.

2007 2008 2009 2010-4.00

-3.00

-2.00

Fig. 11. Capital inflow shocks obtained from a five-country VAR with portfolioinflows and house price.

5. Robustness

In this section we perform a number of robustness checks tocorroborate the findings presented before. A first modification per-tains to the treatment of the variables. Although some of the vari-ables exhibit a trending behavior, the baseline specificationincludes a constant as the only deterministic variable. This is in line

2007 2008 2009 2010-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

Fig. 9. Capital inflow shocks obtained from a five-country VAR with total capitalinflows and house price.

with the bulk of the literature. To nevertheless address that issue,we linearly detrend the GDP, the price level and the house priceseries before the estimation stage. For the remaining variables,i.e. capital inflows, interest rates and the real exchange rate, a timetrend appears to be less plausible. The results of this specificationbased on the otherwise unchanged baseline VAR specification arepresented in Fig. 13. While the size of the effect of capital inflow

2007 2008 2009 2010-4.00

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

Fig. 12. Capital inflow shocks obtained from a five-country VAR with portfolioinflows and equity price.


0 5 10 15-0.50

0.00

0.50

1.00

1.50

Response of GDP

0 5 10 15-20.00

0.00

20.00

40.00

Response of CPI

0 5 10 15-10.00

0.00

10.00

20.00

Response of reer

0 5 10 15-0.50

0.00

0.50

1.00

1.50


0 5 10 15-0.50

0.00

0.50

1.00


0 5 10 15-0.15

-0.10

-0.05

0.00

0.05


0 5 10 15-0.05

0.00

0.05

0.10

Fig. 13. Impulse responses after a capital inflow shock obtained from a five-country VAR with total capital inflows and house prices and detrended GDP, price level and houseprice series.


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15-0.50

0.00

0.50

1.00

1.50

Response of GDP

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15-0.50

0.00

0.50

1.00

1.50

2.00

Response of CPI

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15-0.05

0.00

0.05

0.10

Response of reer

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15-0.05

0.00

0.05

0.10

0.15


0 1 2 3 4 5 6 7 8 9 10 11 12 13-0.30

-0.20

-0.10

0.00

0.10

0.20


0 1 2 3 4 5 6 7 8 9 10 11 12 13-0.02

-0.01

-0.01

-0.00

0.00

0.01


0 1 2 3 4 5 6 7 8 9 10 11 12 13-0.06

-0.04

-0.02

0.00

0.02

Fig. 14. Impulse responses after a capital inflow shock obtained from a six-country VAR with total capital inflows and house prices excluding the financial crisis.


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

2 4 6 8 10 12 14 16

all countriesexcl. HKGexcl. KORexcl. SGP

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

2 4 6 8 10 12 14 16

all countriesexcl. MALexcl. THAexcl. TWN

Fig. 15. Impulse responses of house prices after a capital inflow shock obtainedfrom a VAR in which a given country is excluded.

-0.8

-0.4

0.0

0.4

0.8

1.2

1.6

2 4 6 8 10 12 14 16

all countriesexcl. HKGexcl. KORexcl. SGP

-0.8

-0.4

0.0

0.4

0.8

1.2

1.6

2 4 6 8 10 12 14 16

all countriesexcl. MALexcl. THAexcl. TWN

Fig. 16. Impulse responses of equity prices after a capital inflow shock obtainedfrom a six-country VAR in which a given country is excluded.

8 Carstensen et al. (2009) use a data-driven approach to split a panel into twodisjoint groups. Although appealing, this procedure is not feasible here due to thesmall number of countries.


shocks on house prices remains roughly unchanged, the effect is nolonger significant.

Second, a large fraction of the available observations stems fromthe recent financial crisis. To isolate the effect of the crisis, we esti-mate the model up to 2008:2, i.e. we close the estimation windowbefore the Lehman collapse in the second half of 2008 and theresulting financial meltdown. We chose the VAR II model for thatexercise to see whether the negative interest rate response follow-ing a capital inflow shock observed before is indeed due to aggres-sive policy measures to combat the crisis. The results are presentedin Fig. 14. While the effect of shocks on house prices is smaller inthe pre-crisis sample than in the complete sample presented be-fore, the negative response of the short-term interest rate remainspuzzling. This probably reflects the drastic interest rate cuts in Sin-gapore starting in mid-2006. Singapore is not covered by the VAR Imodel which exhibits a positive interest rate response for the com-plete sample period.

Finally, all impulse response functions presented in this paperare constructed using the identification scheme shown before. An-other set of restrictions, hence, could alter the dynamic responses.Additional results (which are available upon request) reveal thatlifting the restriction on the real exchange rate appreciation has al-most no effect on the results. Furthermore, dropping the restrictionon the long-term interest rate leaves the results unchanged. Therestriction of a positive impact response on GDP matters for theVAR II but not the VAR I. This probably reflects the increased focuson the financial crisis in the former VAR setup that would other-wise be dominated by the fallout from the global recession in2008/2009.

To summarize, our results appear reasonably robust with re-spect to selected modifications of the empirical specification. Inlight of the short time span available for estimation, however,the empirical evidence remains tentative.

6. Cross-country heterogeneity

The choice of a panel model is likely to obscure important cross-country differences in the dynamic responses to shocks. We mainlyresort to panel techniques to cope with the small sample size. Inaddition, the cross-sectional dimension is small due to data avail-ability. Thus, the estimation of country-specific VAR models is noviable alternative.8

To address the extent of cross-country heterogeneity in the re-sponses to capital inflow shocks despite the small dimension of thepanel at hand, we estimate the VAR II model repeatedly and ex-clude each country in turn. This gives us six impulse response func-tions, each for a set of five out of the available six countries.Suppose we exclude country j from the VAR. Comparing the im-pulse response functions of the overall model with that obtainingwithout country j allows us to roughly assess country j’s contribu-tion to the overall findings.

Fig. 15 reveals that excluding Hong Kong, Korea or Singapore re-sults in smaller effects of capital inflow shocks on house pricescompared to the full model. The upper panel of the graph shows


that the responses of house prices in the remaining five-countryVAR model are all smaller than the impulse response of the full,i.e. six-country, VAR. Take the response four quarters after theshock. Excluding one of these three countries, the response isroughly half as strong as the benchmark impulse response includ-ing all countries. Excluding Malaysia, Thailand or Taiwan, in con-trast, leads to stronger responses as shown in the lower panel ofFig. 15. This, in turn, implies that the response are exceptionallystrong in Hong Kong, Korea and Singapore and relatively weak inthe remaining set of countries. Since these findings reflect onlythe marginal impact on the panel of excluding one country, thecross-country differences documented here can be interpreted asconservative estimates of the true degree of heterogeneity.

Two factors could, in principle, be responsible for the observeddifferences in the responses to capital inflow shocks across coun-tries: (1) mortgage market characteristics as surveyed in Zhu(2006), Glindro et al. (2011) and Sá et al. (2011) and (2) the mon-etary policy response to capital inflows. If different mortgage mar-ket characteristics across Asian economies are behind the differentresponse patterns, the pattern should be different for equity priceresponses which are not affected by institutional details of therespective housing markets. If, however, monetary policy explainscross-country differences, the responses of equity prices should besimilar as equity and house price responses are likely to be simi-larly affected by the monetary policy stance. In other words, thedifferences between the house price responses and equity price re-sponses contain information about the underlying sources ofheterogeneity.

Fig. 16 reports the same exercise based on the VAR II modelwith equity prices. The results are striking: the response of equityprices to capital inflows leads to the same grouping of countriesthan before. We clearly see that the exclusion of Hong Kong, Koreaand Singapore leads to a much smaller response in the remainingfive-country panel. Put differently, in the three countries the stockmarket responses are exceptionally strong. This exactly corre-sponds to the grouping of countries in the VAR on house prices dis-cussed before. Hence, this points to monetary policy responses asthe key factor determining the strength of the asset price responsesand reflects the focus on maintaining the currency board in HongKong and the managed exchange rate in Singapore which preventsthe monetary authorities to tighten policy. Moreover, many otherAsian central banks, among them the Bank of Korea, are reluctantto tighten policy fearing that a wider return differential with re-spect to mature economies would attract even larger capitalinflows.9

While the results corroborate the notion that macroeconomicpolicy explains cross-country differences, the findings not neces-sarily imply that macroprudential policies such as loan-to-value(LTV) limits, which were introduced in Hong Kong and Korea, wereineffective.10 Given the bluntness of monetary policy as an instru-ment to contain asset price booms (see Crowe et al. (2011) for thispoint), the evidence presented here is consistent with the view thatmacroprudential measures haven not been used boldly enough toprevent bubbly house price developments.

7. Conclusions

This paper estimates the impact of capital inflow shocks onproperty prices and equity prices in Asian economies using a panelVAR with sign restrictions for a post-2000 sample. The key results

9 See IMF (2011b) for a discussion of the remaining room of regional central banksto raise interest rates in light of capital inflows.

10 The empirical case for macroprudential policies in Asia such as LTV ratios andother measures is discussed in Igan and Kang (2011), Wong et al. (2011) and Craig andHua (2011).

are that, first, capital inflow shocks significantly push up housingprices and stock prices and, second, are twice as important forthe development of asset prices than in OECD countries. A thirdfinding reveals that cross-country differences in the responses tocapital inflow shocks are not due to housing market characteristicsor the use of macroprudential policies directed to contain propertyprice bubbles. Instead, the evidence is consistent with the viewthat differences in macro policies, e.g. monetary policy, are thesource of heterogeneity across countries.

The ebb and flow of capital inflows over the recent years did in-deed contribute to the observed surge in house prices. This also im-plies that ongoing inflows of capital pushed to emergingeconomies by loose monetary conditions in advanced economiesposes serious risks to financial stability in the recipient countries.

Here we quantify the impact of shocks to push-factors overwhich domestic policies have no control. While our evidence sug-gests that a monetary tightening could dampen the effect on assetmarkets, raising interest rates in order to dampen property pricesincreases is certainty too blunt an instrument to be applied innon-crisis periods. A deeper empirical analysis of the impact ofmacroprudential policy measures is needed, which have recentlybeen employed throughout the region, to assess their effectiveness.It will also be informative to see how an unwinding of capital in-flows, probably due to an eventual tightening of monetary condi-tions in mature economies, will affect emerging economies’ assetprices.

Acknowledgments

This paper was written while I enjoyed the generous ospitalityof the Hong Kong Institute for Monetary Research. I am grateful toan anonymous referee as well as seminar articipants at the HKIMRand the OeNB workshop on ‘‘Financial Markets and Real EconomicActivity’’ for very helpful comments on an earlier draft. I am in-debted to Katrin Assenmacher-Wesche for providing arts of theRATS code used in this paper and Stefan Gerlach, who was involvedin the early stages of this project.

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Capital inflows and asset prices: Evidence from emerging Asia