| Comparison of Interest rate differentials to exchange rate movement for Indian Rupee vis-a`-vis US Dollar| ICF | | | | | Introduction4 Literature Review5 Interest Rate Parity6 Methodology10 Data10 Spot Exchange Rate Data:10 Forward Rate Data:10 Interest Rate Data for India:11 Interest Rate Data for US:11 Analysis and Discussion11 Deviations from Interest Rate Parity (DIRP):11 One Month Forwards:11 3 Month Forwards:13 6 Month Forwards:14 9 Month Forwards:15 12 Month Forwards16 Econometrics17 Unit testing for validating stationary data17

Regression Analysis18 Analysis18 One-month forward18 Three-month Forward20 Six Month Forward21 Nine Month Forward22 Twelve Month Forward24 Analysis using Capital Inflows25 Conclusion27 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 in which the law of one price holds forfinancial assets- i. e. omestic 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 Review The 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. nterest 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 Parity Interest 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. Methodology

Interest 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. 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: Expected Value (Mean) of the Error Term: -0. 1495 Standard Deviation of the Error Term: 0. 199200692 t statistic = (Actual Value of Error – Expected Value of Error ) / Standard Deviation of Error = ( -0. 1495 – 0)/ 0. 199200692 = -0. 750568576 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. 6 Month Forwards:

Expected Value (Mean) of the Error Term: -0. 3559 Standard Deviation of the Error Term: 0. 384524814 t statistic = (Actual Value of Error – Expected Value of Error ) / Standard Deviation of Error = (-0. 3559 – 0)/ 0. 384524814 = -0. 925596147 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. 9 Month Forwards: Expected Value (Mean) of the Error Term: -0. 5879 Standard Deviation of the Error Term: 0. 571277246 statistic = (Actual Value of Error – Expected Value of Error ) / Standard Deviation of Error = (-0. 5879 – 0)/ 0. 571277246 = -1. 029028333 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. 12 Month Forwards Expected Value (Mean) of the Error Term: -0. 8329 Standard Deviation of the Error Term: 0. 75560996 t statistic = (Actual Value of Error – Expected Value of Error ) / Standard Deviation of Error = (-0. 8329– 0)/ 0. 75560996 -1. 102272763 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. 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. Econometrics In 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 data

To 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. 5 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 Analysis Regression 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 forward

For one month forward, the unit test for validating stationary time series data shows deviations from basic assumption. | Coefficients| Standard 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. 008454029| Forward Premium| -0. 714246068| 0. 143478264| -4. 978078543| 1. 03655E-05| -1. 003407506| -0. 425084631| -1. 003407506| -0. 425084631| | Coefficients| Standard Error| t Stat| P-value| Lower 95%| Upper 95%| Lower 95. 0%| Upper 95. 0%| Intercept| 0. 00388982| 0. 000208181| 1. 8684764| 0. 0683628| -3. 058E-05| 0. 0008085| -3. 058E-05| 0. 000808544| Interest Rate| -0. 088586245| 0. 056048638| -1. 5805245| 0. 1211496| -0. 2015449| 0. 0243724| -0. 201544851| 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. 92(Interest rate differential) Regression Statistics| Multiple R| 0. 4575725| R Square| 0. 2093726| Adjusted R Square| 0. 1918031| Standard Error| 0. 02204885| Observations| 47| R-square value shows the explained variation as close to 21% only which is very low. The trend can be shown as 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 Forward

Similar 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. | Coefficients| Standard Error| t Stat| P-value| Lower 95%| Upper 95%| Lower 95. 0%| Upper 95. 0%| Intercept| 0. 0008066| 0. 0050935| 0. 158365| 0. 8749282| -0. 0094725| 0. 0110857| -0. 0094725| 0. 0110857| FORWARD PREMIUM| -0. 2610094| 0. 10265673| -2. 5425449| 0. 0147797| -0. 468179| -0. 0538397| -0. 468179| -0. 0538397| | Coefficients| Standard Error| t Stat| P-value| Lower 95%| Upper 95%| Lower 95. 0%| Upper 95. 0%| Intercept| 0. 012137| 0. 00061256| 1. 981294| 0. 0541299| -2. 253E-05| 0. 0024499| -2. 253E-05| 0. 0024499| INTEREST 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: forward premium = 0. 112 -10. 039(Interest rate differential)

Regression Statistics| Multiple R| 0. 73173712| R Square| 0. 53543921| Adjusted R Square| 0. 52463547| Standard Error| 0. 03392052| Observations| 45| The 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 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.

Six Month Forward Unit testing for time series data for 6 month forward shows that both the data series are stationary in nature. | Coefficients| Standard Error| t Stat| P-value| Lower 95%| Upper 95%| Lower 95. 0%| Upper 95. 0%| Intercept| -0. 0004406| 0. 0050369| -0. 08748| 0. 9307376| -0. 0106286| 0. 0097474| -0. 0106286| 0. 0097474| FORWARD PREMIUM| -0. 0844871| 0. 0624442| -1. 3530023| 0. 1838466| -0. 2107924| 0. 0418182| -0. 2107924| 0. 0418182| | Coefficients| Standard Error| t Stat| P-value| Lower 95%| Upper 95%| Lower 95. 0%| Upper 95. 0%| Intercept| 0. 0024076| 0. 0012679| 1. 8988651| 0. 649984| -0. 000157| 0. 0049722| -0. 000157| 0. 0049722| INTEREST 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 forward premium = 0. 1861 -8. 334(Interest rate differential) Regression Statistics| Multiple R| 0. 773163168| R Square| 0. 597781284| Adjusted R Square| 0. 87725816| Standard Error| 0. 051177283| Observations| 42| Explained variation in this case is close to 60% which shows a good relation. The trend analysis is shown as follows 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 Nine Month Forward Unit testing for time series data for 9 month forward shows that both the data series are stationary in nature. Coefficients| Standard Error| t Stat| P-value| Lower 95%| Upper 95%| Lower 95. 0%| Upper 95. 0%| Intercept| -0. 0025426| 0. 005334| -0. 4766741| 0. 636475| -0. 0133604| 0. 0082752| -0. 0133604| 0. 0082752| FORWARD PREMIUM| -0. 0654285| 0. 0494939| -1. 3219517| 0. 1945231| -0. 1658068| 0. 0349497| -0. 1658068| 0. 0349497| | Coefficients| Standard Error| t Stat| P-value| Lower 95%| Upper 95%| Lower 95. 0%| Upper 95. 0%| Intercept| 0. 0035078| 0. 0019343| 1. 8134906| 0. 0780998| -0. 0004151| 0. 0074308| -0. 0004151| 0. 0074308| INTEREST RATE DIFF| -0. 0973005| 0. 0582829| -1. 6694522| 0. 1037025| -0. 2155038| 0. 0209027| -0. 155038| 0. 0209027| 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 9-month forward is as follows forward premium = 0. 2068 -6. 227(Interest rate differential) Regression Statistics| Multiple R| 0. 671420292| R Square| 0. 450805208| Adjusted R Square| 0. 435962106| Standard Error| 0. 080495681| Observations| 39| Explained variation in this case is close to 45% which shows a linear relation. The trend analysis is shown as follows

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 mid 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 Twelve Month Forward Unit testing for time series data for 12 month forward shows that both the data series are stationary in nature. | 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. 170544| 0. 0119167| FORWARD PREMIUM| -0. 0737933| 0. 0556671| -1. 3256185| 0. 1940702| -0. 1870488| 0. 0394622| -0. 1870488| 0. 0394622| | Coefficients| Standard Error| t Stat| P-value| Lower 95%| Upper 95%| Lower 95. 0%| Upper 95. 0%| Intercept| 0. 00471| 0. 0026966| 1. 7466168| 0. 0900071| -0. 0007764| 0. 0101964| -0. 0007764| 0. 0101964| INTEREST RATE DIFF| -0. 0968552| 0. 0606361| -1. 5973179| 0. 1197277| -0. 2202204| 0. 02651| -0. 2202204| 0. 02651| 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 12-month forward is as follows forward premium = 0. 1744 -4. 094(Interest rate differential) Regression Statistics| Multiple R| 0. 508144918| R Square| 0. 258211257| Adjusted R Square| 0. 236393941| Standard Error| 0. 112197183| Observations| 36| Explained variation in this case is close to 25% which shows a weak relation. The trend analysis is shown as follows 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 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 Inflows The 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 Errors| 2006| 0. 0475| 0. 42853637| 2007| -0. 0788| 0. 415671665| 2008| -0. 7533| 0. 569689308| 2009| -0. 3131| 0. 250006667| 2010| -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| 69,557| Foreign Investment Flows 2006-2010, (Source: RBI Website) 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. streas. 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”