IRP

Comparison of Interest rate differentials to exchange rate movement for Indian Rupee vis-a`-vis US Dollar

ICF

INTRODUCTION.................................................................................................................................................. 4

LITERATURE REVIEW........................................................................................................................................... 5

INTEREST RATE PARITY........................................................................................................................................ 6


METHODOLOGY................................................................................................................................................ 10

DATA.......................................................................................................................................................................10Spot Exchange Rate Data:.................................................................................................................................10Forward Rate Data:...........................................................................................................................................10Interest Rate Data for India:..............................................................................................................................11Interest Rate Data for US:.................................................................................................................................11

ANALYSIS AND DISCUSSION.............................................................................................................................. 11

DEVIATIONS FROM INTEREST RATE PARITY (DIRP):..........................................................................................................11One Month Forwards:.......................................................................................................................................113 Month Forwards:............................................................................................................................................136 Month Forwards:............................................................................................................................................149 Month Forwards:............................................................................................................................................1512 Month Forwards...........................................................................................................................................16

ECONOMETRICS................................................................................................................................................ 17

UNIT TESTING FOR VALIDATING STATIONARY DATA............................................................................................................17REGRESSION ANALYSIS................................................................................................................................................18

ANALYSIS.......................................................................................................................................................... 18

ONE-MONTH FORWARD...............................................................................................................................................18THREE-MONTH FORWARD............................................................................................................................................20SIX MONTH FORWARD................................................................................................................................................21NINE MONTH FORWARD.............................................................................................................................................22TWELVE MONTH FORWARD.........................................................................................................................................24

ANALYSIS USING CAPITAL INFLOWS.................................................................................................................. 25

CONCLUSION.................................................................................................................................................... 27

Introduction

Financial openness exists when residents of one country are able to tradeassets with residents

of another country, i.e. when financial assets are traded goods. Aweak definition of complete

financial openness, which one might refer to as financialintegration, can be given as a situation

ICF Group Project Page 2


in which the law of one price holds forfinancial assets- i.e. domestic and foreign residents trade

identical assets at the sameprice. A strong definition would add to this the restriction that

identically definedassets e.g. a six-month Treasury bill, issued in different political jurisdictions

anddenominated in different currencies are perfect substitutes in all private portfolios.

The degree of financial integration has important macroeconomic implications interms of the

effectiveness of fiscal and monetary policy in influencing aggregatedemand as well as the scope

for promoting investment in an economy.The free and unrestricted flow of capital in and out of

countries and the everincreasingintegration of world capital markets can be attributed to the

process ofGlobalization. The benefits of such integration are liquidity enhancement on one

handand risk diversification on the other, both of which are instrumental in makingmarkets more

efficient and also facilitate smooth transfers of funds between lendersand borrowers. India

began a very gradual and selective opening of the domesticcapital markets to foreign residents,

including non-resident Indians (NRIs), in theeighties. The capital market opening picked up pace

during the nineties.

Real interest parity, uncoveredinterest parity and covered interest parity gives a indication of

financial integration of economy.Three definitions of financial integration are as follows:

(i) Real interest parity hypothesis states that international capital flows equalize real

interest rates across countries.

(ii) Uncovered interest parity states that capital flows equalize expected rates of return

on countries’ bonds regardless of exposure to exchange risk.

(iii) Covered interest parity states that capital flows equalize interest rates across

countries when contracted in the same currency.

Only definition (iii) that the covered interest differential is zero is an unalloyed criterion for

“capital mobility” in the sense of the degree of financial market integration across national

boundaries. Condition (ii) that the uncovered interest differential is zero requires that (iii) hold

and that there be zero exchange risk premium. Condition (i) that the real interest differential be

zero requires condition (ii) and in addition that expected real depreciation is zero.

Literature ReviewThe uncovered interest parity (UIP) theory states that differences betweeninterest rates across

countries can be explained by expected changes in currencies.Empirically, the UIP theory is

usually rejected assuming rational expectations, and explanations for this rejection include that



expectations are irrational. There appears to be overwhelming empirical evidence against UIRP,

at least at frequencies less than one year. Other research shows that UIRP holds in long term.

The results of these long horizon regressions are much more positive — the coefficients on

interest differentials are of the correct sign, and most are closer to the predicted value of unity

than to zero. Research done by Ravi Bansal and Magnus Dahlquistconclude that the often

found negative correlation between the expected currency depreciation and interest rate

differential is, contrary to popular belief, not a pervasive phenomenon. It is confined to

developed economies, and here only to states where the U.S. interest rate exceeds foreign

interest rates. Research done for emerging markets by Frank S. Skinner shows that there

isindeed violations in covered interest rate parity in the long-term capital markets andthe source

of these violations is credit risk rather than the size of the economy orliquidity of the foreign

exchange market. The covered interest parity (CIP) postulates that interest rates denominated

in different currencies are equal once you cover yourself against foreign exchange risk. Unlike

the UIP, there is empirical evidence supporting CIP hypothesis. Empirical studies by various

researchers shows that the CIP holds in most cases on the Eurocurrency market (where

remunerated assets have similar default and political risk characteristics) since the collapse of

the Bretton Woods regime in early 1970’s.

In the Indian context, Varma (1997) has undertaken an analysis of the covered interest parity.

He posits a structural break in the money market in India in September 1995, with CIP become

effective from that point on for the first time in the Indian money market. The structural break

itself is attributed to interplay between the money market and the foreign exchange market. The

period after 1995 is however witness to several deviations from the CIP. Varma has used rates

on Treasury bills, certificates of deposit and commercial paper and call money rate to analyze

the Indian money market.

One problem encountered in examining covered interest rate parity is a lack of highquality

observations on long-term interest rates the terms of which are comparableacross different

markets. A ready solution is the interest rate swap market. Thismarket has evolved into one of

the most important international fixed income markets. Benefits of using swap interest rates are

as follows:

a) swap terms and conditions arecomparable across different markets

b) swaps are liquid instruments so high quality information is available even for long

terms in emerging markets

c) swap rates areclosely related to the underlying national bond markets and reflect the

interest ratesavailable for borrowing and investment



In our analysis in this report we have not used swap rates as they are available only for

International swap dealers association members. Literature suggests that zero coupon bond

yields are close proxy for the interest rates.

Interest Rate ParityInterest rate parity is an economic concept, expressed as a basic algebraic identity that relates

interest rates and exchange rates. The identity is theoretical, and usually follows from

assumptions imposed in economic models. There is evidence to support as well as to refute the

concept. In this report, we will analyze the data available to find whether this concept can be

supported or refuted in case of India and US.

Interest rate parity is a non-arbitrage condition which says that the returns from borrowing in one

currency, exchanging that currency for another currency and investing in interest-bearing

instruments of the second currency, while simultaneously purchasing futures contracts to

convert the currency back at the end of the holding period, should be equal to the returns from

purchasing and holding similar interest-bearing instruments of the first currency. If the returns

are different, an arbitrage transaction could, in theory, produce a risk-free return. This can be

shown as

(1+ irs) = (Frs/$/Srs/$) (1+ i$ )

Where

irs= interest rates in India

i$= interest rates in US

Frs/$= Forward exchange rate

Srs/$= Spot exchange rate

Looked at differently, interest rate parity says that the spot price and the forward, or futures

price, of a currency incorporate any interest rate differentials between the two currencies

assuming there are no transaction costs or taxes.IRP is a manifestation of the Law of One

Price (LOP)applied to international money market instruments.



Being an arbitrage equilibrium condition involving the spot exchange rate, IRP has an

immediate implication for exchange rate determination. Reformulating the IRP relationship in

terms of spot exchange rate gives

S = [(1+i$)/(1+irs)] F

Above equation indicates that the forward exchange rate, the spot exchange rate depends upon

relative interest rates. All else equal, an increase in Indian interest rates will lead to higher

foreign exchange value of Indian rupee. This is so because a higher Indian interest rates will

attract capital to India, increasing the demand for Indian rupee. In contrast, a decrease in Indian

interest rates will lower the foreign exchange value of Indian rupee.

In addition to relative interest rates, the forward exchange rates is an important parameter in

spot exchange rate determination. Under certain conditions the forward exchange can be

viewed as the expected future spot exchange rate conditional on all relevant information being

available now

F = E(St+1| It)

Where St+1 is the future spot rate when the forward contract matures and It denotes the set of

information currently available. Hence the final relation will be as follows

S = [(1+i$)/(1+irs)] E (St+1 | It)



Two things are noteworthy here that expectations play a key role in exchange rate

determination. Specifically, the expected future rate is shown to be a major determinant of the

current exchange rate when people expect the exchange rate to go up in future, it goes up now.

People’s expectations thus become self fulfilling. Second, exchange rate expectations will be

driven by the news event. People form their expectations based on the set of information (It)

they possess. As they receive news continuously, they are going to update their expectations

continuously. As a result, the exchange rate will tend to exhibit a dynamic and volatile short term

behavior, responding to various news events. By definition, news events are unpredictable,

making forecasting future exchange rates an arduous task.

When the forward exchange rate F is replaced by the expected future spot exchange rate, we

can rewrite IRP as

(irs – i$)= E(e)= [E(St+1) – St]/St

Above equation states that interest rate differential between a pair of countries is approximately

equal to the expected rate of change in the exchange rate. This relationship is known as

uncovered interest rate parity.

Although IRP tends to hold quite well, it may not hold all the times precisely all the times for at

least two reasons: transaction costs and capital controls. In reality, transaction costs do exist.

The interest rate at which the arbitrager borrows, ia, tends to be higher than the rate at which he

lends, ib, reflecting the bid-ask spread. Likewise, there exists a bid-ask spread in the foreign

exchange market as well. Because of the transaction costs, the IRP line can be viewed as

included within a band around it. The width of band depends upon the size of transaction cost.



Another major reason for deviations from IRP is capital controls imposed by governments. For

various macroeconomic reasons, governments sometimes restrict capital flows, inbound and/or

outbound. Governments achieve this objective by means of jawboning, imposing taxes or even

outright bans on cross border capital movements. These control measures imposed by

governments can be effectively impair the arbitrage process and as a result, deviations from

IRP may exist.

Deviations from IRP (DIRP) can be calculated as follows:

DIRP = [S(1+irs)/ (1+i$) F] - 1

If IRP holds strictly, deviations from it would be randomly distributed, with expected value of

zero.

When IRP does not hold good, the situation gives rises to covered interest rate parity.Assume

that individuals are risk averse. Such anindividual would like to cover himself for any

unexpected currency fluctuationduring the tenure of the deal. Given the forward contract market,

he wouldpurchase a forward contract and use the exchange rate mentioned in the

contract.Then any difference in interest rate should be equated to forward premium. Any

deviation from CIP would suggest that the markets are inefficient,regulations like capital controls

exist and costs like sovereign risk, individualborrowing constraints are not accounted for.

MethodologyInterest rate parity connects the forward rates on a currency pair to the prevailing interest rates

in the respective countries and the existing spot exchange rate. In order to analyze the interest

rate parity relationship, the interest rates were obtained for both India and the US. The spot

exchange rate data between the US dollar and Indian Rupee was also obtained. Using this

data, the expected forward rate was calculated using the interest rate parity relationship which

specifies that higher Interest rates in India compared to the US should lead to the depreciation

of the Indian Rupee in the forward markets. This expected forward rate is compared to the

actual forward rates on currency forwards which are being traded in the market. The error was

calculated between the actual and expected rates and statistical analysis was done for the

same at 95% confidence level.



Data

Spot Exchange Rate Data:The spot exchange rate between Indian Rupee and US dollar (INR/USD) was obtained from

FEDAI (Foreign Exchange Dealers Association of India) website. The data was obtained on a

month end basis beginning September 2006 till July 2010.

Forward Rate Data:The forward rate data was also obtained from FEDAI website. On each date currency forwards

of different maturities are available. The currency forwards are available for different monthly

maturities ranging from 1 month to 9 months. 12 month currency forwards are also available.

Data related to currency forwards for 10 months and 11 months maturity was not available. For

each date, the

Interest Rate Data for India:Interest rate parity assumes default free investments in both the countries. The yield on 1 year

Government of India bonds was taken as a proxy for the default free rate in India. For different

maturities, the interest rates were calculated based on the one year bond yields. The data for

the interest rates was obtained from Fixed Income Money Market and Derivatives Association of

India (FIMMDA).

Interest Rate Data for US:The yields on one year treasury securities were taken as a proxy for the default free rate for the

US. The data for the yields on US treasury securities was obtained from the US Department of

Treasury website.

Analysis and Discussion

Deviations from Interest Rate Parity (DIRP):It measures the difference between the theoretical prediction of the forward rate and the actual

forward rate observed in the derivatives market. If interest rate parity holds, the deviations would

be randomly distributed with an expected value of zero.

DIRP = [(1 + i India) x Spot Rate / (1 + i US) x F ] - 1

The null and alternate hypothesis can be stated as follows:

Null:Expected (DIRP) = 0, Interest Rate parity holds



Alternate: Expected (DIRP) is non zero, Interest Rate Parity does not hold

The analysis was carried out for forwards of 1 month, 3 month, 6 month, 9 month and 12 month

duration. The expected value of DIRP was calculated for each of the forwards and the statistical

test was done at 5% significance level (95% confidence level)

One Month Forwards:The DIRP is plotted as follows. The horizontal axis indicates the number of observations for

which error has been calculated.

0 5 10 15 20 25 30 35 40 45 50

-0.4000

-0.3000

-0.2000

-0.1000

0.0000

0.1000

0.2000

Error (Actual - Expected)

Expected Value (Mean) of the Error Term: -0.0462

Standard Deviation of the Error Term: 0.082246844

t statistic = (Actual Value of Error – Expected Value of Error ) / Standard Deviation of Error

= ( -0.0462 – 0)/ 0.082246844

= -0.561512857

t critical at 95% confidence level for a 2 tailed test = 1.96 or -1.96

Since the calculated value of the t statistic is more than -1.96, the null hypothesis cannot be

rejected and IRP holds at 95% level of confidence.

Similar analysis can be carried out for forwards of other maturities which is as follows



3 Month Forwards:

0 5 10 15 20 25 30 35 40 45 50

-1.0000

-0.8000

-0.6000

-0.4000

-0.2000

0.0000

0.2000

0.4000

0.6000





= ( -0.1495 – 0)/ 0.199200692

= -0.750568576






6 Month Forwards:

0 5 10 15 20 25 30 35 40 45 50

-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000





= (-0.3559 – 0)/ 0.384524814

= -0.925596147






9 Month Forwards:

0 5 10 15 20 25 30 35 40 45 50

-2.5000

-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000





= (-0.5879 – 0)/ 0.571277246

= -1.029028333






12 Month Forwards

0 5 10 15 20 25 30 35 40 45 50

-3.5000

-3.0000

-2.5000

-2.0000

-1.5000

-1.0000

-0.5000

0.0000

0.5000

1.0000

1.5000





= (-0.8329– 0)/ 0.75560996

= -1.102272763




From the above analysis we can infer that Interest Rate Parity holds for maturities up to one

year at 95% level of confidence. For various maturities, the predicted forward rate could be

different from the actual rate, which is evident from the deviations from the horizontal axis in the

graphs, but on an average the differences are not statistically significant.

This is consistent with the literature related to IRP which states that covered interest rate parity

holds in the short run and deviations are observed from the relationship in the long run. Since

the data is available for short duration (having durations less than an year) forwards, we cannot

establish the relation for long term.



EconometricsIn order to find the variation in forward premium with respect to interest rate differential, we

perform the econometric analysis on the available data i.e forward rate, spot rate, interest rate in

India and Interest rate in US. As we know the interest parity relationship can also be rewritten as

(irs – i$)= [E(St+1) – St]/St

In order to find the relationship for forwards of different durations, we performed regression

analysis on interest rate differential (difference of interest rate in India and US) and forward

premium. Regression analysis gives the liner relationship as well as the explained variation in

the relationship. Explained variation analysis helped us in understanding the strength of

relationship and look for the reason of unexplained variation. Unexplained variation can be

attributed to various reasons i.e transactional costs, free capital mobility and other

macroeconomic events.

Before performing the regression model on a time series data, data needs to be validated for

stationary property. For time series data of interest rate differential and forward premium, we

performed unit test for validating the stationary property of data.In case of non-stationary times

series, the estimate of parameters of regression model would be spuriousand biased.

Unit testing for validating stationary dataTo validate the stationary property of time series data, we perform a regression analysis

between two variables, difference between Yt+1 and Ytwith Yt.as independent variable The

regression model can be written as

(Yt+1 - Yt ) = a + b* Yt + Et

After estimating the regression model we perform the hypothesis testing on slope parameter i.e.

Null Hypothesis: b equal to zero

Alternative Hypothesis: b not equal to zero

For validating this, we compare the p-value given by regression analysis for the independent

variable for 95% confidence interval. If the p-value is less than 0.05 we will reject the null

hypothesis otherwise we will accept the hypothesis. After validating the data for stationary

property, we proceed to regression model between forward premium and interest rate

differential.



Basic assumption in performing this analysis is that if the value of b=0 then data is not

dependent of previous time period data which implies time series data is stationary in nature.

Regression AnalysisRegression model for validating the relationship between interest rate differential and forward

premium can be estimated as

Forward premium = a + b * interest rate differential +error

After validating the time series data for various maturity of forwards, we performed the

regression analysis to estimate the relationship and also find the explained variation of forward

premium with respect to interest rate differential which is given by R-square parameter of the

regression analysis. We also plotted the forward premium and interest rate differential with

respect to time to find the variation with time and get the trend of both the variables with

time(Interest rate differential is multiplied by 10 to get clear trend)

Analysis

One-month forwardFor one month forward, the unit test for validating stationary time series data shows deviations from basic assumption.

CoefficientsStandard

Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 0.001325955 0.003536861 0.374896027 0.709538806-

0.005802119 0.008454029-

0.005802119 0.008454029Forward Premium

-0.714246068 0.143478264

-4.978078543 1.03655E-05

-1.003407506

-0.425084631

-1.003407506

-0.425084631


Error t Stat P-valueLower 95%

Upper 95% Lower 95.0% Upper 95.0%

Intercept0.00038898

2 0.0002081811.868476

40.068362

8-3.058E-

050.000808

5 -3.058E-050.00080854

4

Interest Rate

-0.08858624

5 0.056048638

-1.580524

50.121149

6

-0.201544

90.024372

4

-0.20154485

1 0.02437236

As shown in the above tables, p-value for forward premium is less than 0.05 where as it is more

for interest rate differential. Hence, data is not perfect stationary. As one series is stationary, we

proceed with the regression analysis as atleast one of the data series is stationary in nature.



Regression equation for one month forward is as follows:

forward premium = 0.0358 -9.392(Interest rate differential)

Regression Statistics

Multiple R 0.4575725

R Square 0.2093726

Adjusted R Square 0.1918031

Standard Error 0.02204885

Observations 47R-square value shows the explained variation as close to 21% only which is very low. The trend

can be shown as

OCT.2006

DEC.2006

FEB.2007

APR.2007

JUN.2007

AUG.2007

OCT.2007

DEC.2007

FEB.2008

APR.2008

JUN.2008

AUG.2008

OCT.2008

DEC.2008

FEB.2009

APR.2009

JUN.2009

AUG.2009

OCT.2009

DEC.2009

FEB.2010

APR.2010

JUN.2010

AUG.2010

-0.0800

-0.0600

-0.0400

-0.0200

0.0000

0.0200

0.0400

0.0600

0.0800

1M forward CIRP

Forward Premium Interest rate differential

As we can see in the trend analysis, that forward premium and interest rate differential shows a

opposite trend from mid of 2007 to the start of 2009. This can be attributed to economic

downturn when free capital mobility was hampered between India and US as US restored to

more conservative approach.

Three-month ForwardSimilar to one month forward, unit testing shows the same trend when forward premium data is

not stationary in nature where as interest rate differential data is stationary in nature.

Coefficient

sStandard


Upper 95%

Lower 95.0%

Upper 95.0%

Intercept 0.0008066 0.0050935 0.158365 0.8749282

-0.009472

0.0110857

-0.009472

0.0110857



5 5

FORWARD PREMIUM

-0.2610094

0.10265673

-2.542544

90

.0147797 -0.468179

-0.053839

7 -0.468179 -0.0538397



Upper 95%

Lower 95.0% Upper 95.0%

Intercept 0.0012137 0.00061256 1.981294 0.0541299-2.253E-

05 0.0024499-2.253E-

05 0.0024499INTEREST RATE DIFF -0.0998457 0.05524714

-1.8072555

0.0778889

-0.211339 0.0116475

-0.211339 0.0116475

As shown in the above tables, p-value for forward premium is less than 0.05 where as it is more

for interest rate differential. Hence, data is not perfect stationary. As one series is stationary, we

proceed with the regression analysis as atleast one of the data series is stationary in nature.

Regression equation for three month forward is as follows:




R Square 0.53543921



Observations 45The explained variation is good in this case which is close to 54%. This model shows a better

estimate than one month forward. The trend analysis is as follows

DEC.2006

FEB.2007

APR.2007

JUN.2007

AUG.2007

OCT.2007

DEC.2007

FEB.2008

APR.2008

JUN.2008

AUG.2008

OCT.2008

DEC.2008

FEB.2009

APR.2009

JUN.2009

AUG.2009

OCT.2009

DEC.2009

FEB.2010

APR.2010

JUN.2010

AUG.2010

-0.1500

-0.1000

-0.0500

0.0000

0.0500

0.1000

0.1500

0.2000

3M forward CIRP

Forward Premium Interest Rate Differential






more conservative approach.

Six Month ForwardUnit testing for time series data for 6 month forward shows that both the data series are

stationary in nature.

Coefficient

sStandard


Upper 95%


Intercept-

0.00044060.005036

9 -0.087480.930737

6

-0.010628

60.009747

4

-0.010628

6 0.0097474

FORWARD PREMIUM

-0.0844871

0.0624442

-1.353002

30.183846

6

-0.210792

40.041818

2

-0.210792

4 0.0418182



Upper 95%


Intercept 0.0024076 0.0012679 1.8988651 0.0649984 -0.000157 0.0049722 -0.000157 0.0049722INTEREST RATE DIFF -0.0973421 0.0575548

-1.6912952

0.0987579

-0.2137576 0.0190734

-0.2137576 0.0190734

As shown in the above tables, p-value for forward premium is more than 0.05 as well as for

interest rate differential. Hence, data is perfect stationary. As both series are stationary, we

proceed with the regression analysis.

The regression model for 6-month forward is as follows




R Square 0.597781284



Observations 42Explained variation in this case is close to 60% which shows a good relation. The trend analysis

is shown as follows



MAR.2007

MAY.2007

JUL.2

007

SEP.2007

NOV.2007

JAN.2008

MAR.2008

MAY.2008

JUL.2

008

SEP.2008

NOV.2008

JAN.2009

MAR.2009

MAY.2009

JUL.2

009

SEP.2009

NOV.2009

JAN.2010

MAR.2010

MAY.2010

JUL.2

010

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

6M forward CIRP





more conservative approach

Nine Month ForwardUnit testing for time series data for 9 month forward shows that both the data series are


Coefficient

sStandard


Upper 95%


Intercept -0.0025426 0.005334

-0.476674

1 0.636475

-0.013360

40.008275

2

-0.013360

4 0.0082752

FORWARD PREMIUM -0.0654285

0.0494939

-1.321951

70.194523

1

-0.165806

80.034949

7

-0.165806

8 0.0349497

Coefficient

sStandard


Upper 95%


Intercept 0.00350780.001934

31.813490

60.078099

8

-0.000415

10.007430

8

-0.000415

1 0.0074308

INTEREST RATE DIFF -0.0973005

0.0582829

-1.669452

20.103702

5

-0.215503

80.020902

7

-0.215503

8 0.0209027










R Square 0.450805208



Observations 39

Explained variation in this case is close to 45% which shows a linear relation. The trend analysis

is shown as follows

JUN.2007

AUG.2007

OCT.2007

DEC.2007

FEB.2008

APR.2008

JUN.2008

AUG.2008

OCT.2008

DEC.2008

FEB.2009

APR.2009

JUN.2009

AUG.2009

OCT.2009

DEC.2009

FEB.2010

APR.2010

JUN.2010

AUG.2010

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

9M forward CIRP



opposite trend from mid of 2007 to the mid of 2009. This can be attributed to economic





Twelve Month ForwardUnit testing for time series data for 12 month forward shows that both the data series are


Coefficients Standard Error

t Stat P-value Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept -0.0025689 0.0071199 -0.3608029

0.7205463 -0.0170544

0.0119167 -0.0170544

0.0119167

FORWARD PREMIUM

-0.0737933 0.0556671 -1.3256185

0.1940702 -0.1870488

0.0394622 -0.1870488

0.0394622

Coefficient

sStandard


Upper 95%


Intercept 0.004710.002696

61.746616

80.090007

1

-0.000776

40.010196

4

-0.000776

4 0.0101964

INTEREST RATE DIFF -0.0968552

0.0606361

-1.597317

90.119727

7

-0.220220

4 0.02651

-0.220220

4 0.02651








R Square 0.258211257



Observations 36

Explained variation in this case is close to 25% which shows a weak relation. The trend analysis

is shown as follows



SEP.2007

NOV.2007

JAN.2008

MAR.2008

MAY.2008

JUL.2

008

SEP.2008

NOV.2008

JAN.2009

MAR.2009

MAY.2009

JUL.2

009

SEP.2009

NOV.2009

JAN.2010

MAR.2010

MAY.2010

JUL.2

010

-0.4000

-0.2000

0.0000

0.2000

0.4000

0.6000

0.8000

12M forward CIRP






As shown in the data analysis that IRP holds for 1-month to 12-month IRP, regression analysis

shows the liner variation in IRP. The trend analysis of all the forward premium and interest rate

differential shows the deviation for all period forwards which can be contributed to global

macroeconomic crisis which had its direct in free capital mobility between India and US. Even

before the crisis, as there was not perfect capital mobility in India, the low value of R-square can

be attributed to macroeconomic policy of both India and US.

Analysis using Capital InflowsThe deviations from Interest Rate Parity (DIRP) have been calculated for the years 2006 to

2010. The deviations are equal to the difference between the values of actual forward rates and

calculated value of forward rate using the IRP formula. If IRP holds, then the expected value of

DIRP should equal zero. The calculated deviations are as follows:

Year Average Deviation from IRP Standard Deviation of Errors2006 0.0475 0.1428536372007 -0.0788 0.415671665



2008 -0.7533 0.5696893082009 -0.3131 0.2500066672010 -0.3734 0.255550415

The data indicates that the mean error (Expected value of DIRP) is highest for the year 2008-09

as compared to other years. Interest rate parity theory assumes free capital mobility between

two countries. Restriction on free capital mobility causes deviations from the IRP. During the

period 2008-09 the global economy witnessed a severe recession. This caused heavy capital

outflows from the Indian markets as foreign institutional investors (FII) withdrew the money

invested in Indian markets. Although the interest rates in India have always been higher

compared to the US, deviation from IRP could be observed due to the capital outflows from the

economy instead of capital inflows. The foreign investment flows are outlined as follows:

Year Direct Investment (USD million)

Portfolio Investment (USD Million)

Total (USD Million)

2006-07 22,826 7,003 29,829

2007-08 34,835 27,271 62,106

2008-09 35,180 –13,855 21,325

2009-10 37,182 32,375 69,557Foreign Investment Flows 2006-2010, (Source: RBI Website)

2006-07 2007-08 2008-09 2009-10

-20,000

-10,000

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

22,826

34,835 35,18037,182

7,003

27,271

-13,855

32,37529,829

62,106

21,325

69,557

Foreign Investment Inflows

Direct Inv.Portfolio Inv.Total

Foreign Investment flows witnessed a marked decrease during the period 2008-09. This is

consistent with the larger deviations observed from the interest rate parity.



Conclusion

From the above analysis we can conclude that deviations from IRP are not statistically

significant in the short run. More comprehensive analysis can be carried out using bid-ask

prices of forward contracts as well as currency rates along with the incorporation of different

currency pairs to account for the macroeconomic policies of different countries related to capital

mobility.



References

i. http://www.fimmda.org/Information_Center/Statistics/ASP/stats.asp

ii. http://www.fedai.org.in/

iii. http://www.ustreas.gov/

iv. http://www.rbi.org.in/

v. http://www.sebi.gov.in/

vi. http://www.indiastat.com/

vii. Vipul Bhatt and ArvindVirmani, 2005, “Global Integration of India’s Money Market:

Interest Rate Parity in India”, Indian Council for Research on International Economic

Relations, New Delhi Working Paper

viii. Menzie Chinn, 2007,”World Economy – Interest Rate Parity I”, Princeton Encyclopedia of

the World Economy

ix. O.Pipatchaipoom_and Stefan C. Norrbin, 2006, “Reexamining Real Interest Rate Parity”,

School of Business, Stanford University

x. DogaUnay, 2005, “A Note on Interest Rate Parity”


http://www.indiastat.com/

http://www.sebi.gov.in/

http://www.rbi.org.in/

http://www.ustreas.gov/

http://www.fedai.org.in/

http://www.fimmda.org/Information_Center/Statistics/ASP/stats.asp

Documents

IRP