exchange rate and its determinants in india

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Exchange Rate and Its Determinants in India

Submitted in partial fulfillment of the requirements for the degree of

B.A. (Hons.) Business Economics

Shivaji College, University of Delhi

By

Akshay Jain

(Roll no. 2075)

Supervisor:

Mr. Abhishek Kumar


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ACKNOWLEDGEMENT

I am grateful to God, for enabling me to complete this project. I

would not be going to do justice in presenting my work withoutmentioning the people around me who have been inextricably related

with the completion of this task.

I would like to express our heartfelt thanks to my lecturer Mr.

Abhishek Kumar for his support and guidance, which he rendered

throughout the study. It could not have been possible to accomplishthis without his thoughtful guidance and expertise.

Finally, for any errors, omissions and shortcomings in the writing of

the report only I am responsible for which I hope that all concerning

regards of this report will forgive me.

Akshay Jain


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Table of Contents

Executive Summary (page 4)

Introduction (page 5)

Module 1 Literature Review

A. Macroeconomic Theories (page 6)

B. Empirical Studies (page 10)

Module 2 Description of Data

A. Introduction (page 13)

B. Source (page 14)

C. Graphs (page 14)

Module 3 Methodology

A. Model (page 19)

B. Assumptions (page 21)

Module 4 Analysis and Conclusion

A. Regression Results (page 25)

B. Conclusions (page 27)

References (page 30)

Appendix (page 31)

Evaluation sheets (page 33-34)


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EXECUTIVE SUMMARY

This study explores the complex relationship between the exchange rate and its possible

macroeconomic determinants in India. There can be infinite variables but for this study the

variables taken are foreign exchange reserves, call rate, bank rate, and money supply,

inflation differentials between domestic and foreign country, index of industrial production,

short run and long run yield differentials between domestic and foreign Treasury Bills. Lag

effects are also introduced.

The study shows that three of the variables that are introduced do not add any explanatory

power to the model which is shown by using Granger Causality test, these variables are

money supply, short run and long run yield differentials between domestic and foreign

Treasury Bills. All the other variables come out to be significant determinants of the

fluctuations in the exchange rate. The R-square (goodness of fit) of the regression model

come out to 47%. It means that 47% of variations in the rate of exchange are explained by the

variables included in the model.

The model follows all the assumptions of the classical linear regression model like, no

autocorrelation, no multicollinearity, error terms are normally distributed and zero covariance

between the error term and explanatory variables.

The variables and its lag effects come out to be significant and in accordance with the

macroeconomic theory. Foreign exchange reserve has direct and negative relationship with

exchange rate. Inflation differential has positive effect but the effect comes out after 2-3

months. Call rates, and Bank rates have mixed effects, sometimes positive and sometimes

negative. Index of industrial production, as expected, has negative effect on exchange rate.


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INTRODUCTION

In the area of international economics, one of the basic issues that are not resolved till now is

regarding the determination of exchange rate. Foreign exchange rate is the price of a unit of

foreign currency in terms of the domestic currency. In a floating exchange rate mechanism,

foreign exchange rate is determined much in the same way as the price of any commodity in

a free market economy ,i.e., demand and supply forces determine the exchange rate.

Appreciation or depreciation of the domestic currency basically depends on the supply of

foreign exchange reserves, liquidity conditions in the economy as determined by money

supply, gross domestic product of the economy, inflation differentials of the concerned

economies, central banks policy intentions and differences in the interest yield on dated

securities of domestic and foreign economies.

In this study the aim is to analyze and interpret the impact of various macroeconomic

variables stated above that are responsible for the fluctuations in the exchange rate in India.

The present research tests validity of this hypothesis in association with the exchange rate

between the Indian rupee and the US dollar. US dollar is used here because US is the single

largest trading partner of India and it is the major international currency.

Module 1 deals with the macroeconomic literature regarding the exchange rate determination

and Empirical studies for the same, i.e., past research done in exchange rate determination.

Module 2 is about the data, i.e., the data that is used in this study, its source, statistics and

graphical presentation.

Module 3 is about the method that has been used in the study to analyze the determinants of

exchange rate in India. Multiple linear regression models have been used (ordinary least

squares).

Module 4 describes the interpretation of the exercise and conclusion of the study.


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MODULE 1 LITERATURE REVIEW

A.MACROECONOMIC THEORIES

FOREIGN EXCHANGE RESERVES:

These are the reserves of foreign currency held with the central bank of domestic economy.

These are accumulated by continual surpluses in the overall balance of payments accounts.

This accumulation causes an increase in the supply of foreign exchange in the market and

shifts the supply curve of foreign exchange to the right. This shift results in excess supply and

results in an appreciation of domestic currency as depicted in the figure below.

INTEREST YIELD DIFFERENTIALS:

The relation between short-term and long-term interest yield differentials and exchange rate

is complex. There are two views regarding the relationship between the interest rate and

exchange rate. According to one view uncovered interest parity theory which implies thatdomestic interest rate is the sum of world interest rate and expected depreciation of home

currency is the basis of exchange rate determination. In other words, the interest rate

differential between domestic and world interest rate is equal to the expected change in the

domestic exchange rate. Therefore, a higher interest differential would attract capital inflows

and result in exchange rate appreciation. On the other hand, monetarists believe that higher

interest rate reduces the demand for money which leads to depreciation of currency due to

high inflation.


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INFLATION DIFFERENTIAL:

There is a direct relationship between domestic and world inflation differential and domestic

exchange rate. In other words, a higher domestic inflation results in high domestic exchange

rate depreciation. This is so because an increase in domestic inflation as compared to world

inflation would increase the domestic demand for foreign commodities and lowers the foreign

demand for domestic commodities, which, in turn, would lead depreciation of domestic

currency to maintain the exchange rate as per the purchasing power theory. Similarly a

decrease in domestic inflation as compared to world inflation causes appreciation of domestic

currency. Therefore, the higher the inflation differential between domestic and foreign

countries, the higher will be the depreciation of domestic currency and vice versa. This theory

is called the Purchasing Power Parity.

PD = PF*ET

ER = PD/PF

LN(ER) = LN(PD/PF) (taking log both sides)

(ERt ERt-1)/ERt-1 = (PDt PDt-1)/PDt-1 (PFt PFt-1)PFt-1

% change in ER = Domestic Inflation Foreign Inflation

Where,

ER = exchange rate domestic currency / foreign currency

PD= price level in domestic country

PF = price level in foreign country

t = time

CENTRAL BANK INTERVENTION

Currency Appreciation means that domestic price increases relative to foreign price which

implies less exports and more imports (as imports become more competitive). Currency

depreciation means that domestic price falls relative to the foreign price which implies that

domestic goods become more cheaper for other countries, i.e., more exports and less imports

(as imports become costly). The goal of intervention is to alter the demand for one currency

by changing the supply of another and thus affect the exchange rate


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Call rate and Bank rate: Call rate is the inter-bank interest rate on funds that are not

deposited for a fixed period. It relates to amount deposited for an indefinite time with a bank.

This rate of interest is used in this study to capture the effect of short term interest

fluctuations on the foreign exchange rate.

Bank rate refers to the rate of interest at which the Central Bank lends short term loans to

commercial banks. This rate of interest is used in this study to capture the effect of long term

interest fluctuations on the foreign exchange rate.

According to Mundell-Fleming model, an increase in interest rate is necessary to stabilize the

exchange rate depreciation and to curb the inflationary pressure and thereby helps to avoid

many adverse economic consequences. The bank increases the interest rate to control the

money supply in the economy. The high interest rate policy is considered important for

several reasons. Firstly, it raises the attractiveness of domestic financial assets as a result of

which capital inflow takes place and thereby limiting the exchange rate depreciation.

Secondly, it not only reduces the level of domestic aggregate demand but also improves the

balance of payment position by reducing the level of imports.

Liquidity (money supply):The growth of broad money and foreign exchange reserves

indicate increased liquidity in the economy. Such an increase in the liquidity is expected to

cause depreciation in the exchange rate. An anticipation of inflation due to increased liquidity

and increase in the aggregate demand are two major causes behind such depreciation.

However, an increase in the foreign exchange reserves also implies an increase in the supply

of foreign currency, which often results in appreciation of the domestic currency. Another

aspect is the process of sterilization; it is a central bank policy of altering the domestic credit

extended by it in an equal and opposite direction to the variations in the foreign exchange

reserves so that the monetary base (M0) remains unchanged. Major reason for increase in

money supply is the rising Fiscal Deficit due to automatic monetization by RBI but after an

agreement in 1999 fiscal deficit is being monetized by borrowings.


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INDEX OF INDUSTRIAL PRODUCTION:

Index of Industrial Production (IIP) is one of the Prime indicators of the macroeconomics for

the measurement of trend in the behavior of the Industrial Production over a period of time

with reference to a chosen base year. It indicates the relative change of physical production in

the field of industries during a specified year as compared to previous year. Index of

Industrial Production (IIP) in simplest terms is an index which details out the growth of

various sectors in an economy.

Aggregate Income Equation is given by

GDP = C + I + G + (X M) (Exports Imports)

Now exports and imports depend on the exchange rate. If there is a rise in the GDP, keeping

other things constant, then it must match with equal rise in the trade balance, i.e., X-M. Trade

balance will improve when there is a depreciation of domestic currency to increase exports

and decrease imports. But this hypothesis is not true in the short run. In fact, currency

depreciation worsens the trade balance in the short run and reduces the GDP of a country.

Trade surplus arises in medium or long run when the sum of the elasticity (in absolute values)

of the demand for imports and exports with respect to real exchange rate is greater than one.

This theory is called as the Marshall - Lerner condition.

Thus there is a negative relation in GDP and currency depreciation in the short run and

positive relation in the long run.

Note that here IIP is used as a proxy for GDP.


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B.EMPIRICAL STUDIES

Macroeconomic Fundamentals and Exchange Rate Dynamics in India: Some

Survey Results - N R Bhanumurthy

The study examines the relevance of macroeconomic models in exchange rate determination

in India. For this, the study has undertaken a primary survey, with the help of structured

mailed questionnaire, on the Indian foreign exchange dealers to understand the dynamics of

the market. The sample of the study is 91 dealers (24% of the total dealers). The findings

from the primary survey is that majority of the dealers feel in the short and medium term, the

changes in exchange rate is not influenced by the changes in macro fundamentals, rather is

basically influenced by the micro variables like order flow, market movement, speculation,

Central Bank intervention etc.. But in the long run, still it is the macro fundamentals that

determine the exchange rates.

The objectives of the study would be as follows:

1) To test the importance of both macro and micro variables in determining exchange rate

movements in different time horizons by using primary information;

2) To find out the predictability of exchange rates in different time horizons;

3) To analyze the effects of speculation and Central Bank intervention on the rate movement

Macroeconomic fundamentals indeed have a role in the exchange rate determination, but it is

not in the intra-day. Both speculation and the central bank intervention are the major

determinants. This vindicates the impression that in India Central Bank plays a spoil sport

in the foreign exchange market activity and the rates move accordingly.

The study finds from the dealers perception that more than fundamentals, micro variables

have more significant impact on the exchange rates in the intra-day but fundamentals are

more useful in predicting the rates in the long run. This is a new finding for any developing

countries foreign exchange market. These results might differ between the countries as it

depends on the specific countrys market regulations and the economy itself.


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Interest, Inflation, Bank Intervention and Exchange Rates - Pradyumna Dash

The objective of this study is to analyze the relationship between exchange rate, interest rate

and inflation differentials and central bank intervention.

The model used is as follows:

ERt=C+1IRt+ 2INFDIFFt+3INTERt+Ut ..(1)

Where,

ER= Exchange Rate, IR=Interest Rate, INFDIFF= Inflation differential between domestic

and foreign countries, INTER= Net intervention by the Central Bank, C=constant, and t is

time period.

It has been found that exchange rate in India can be stabilized or defended by raising interest

rate. But even if the raising of interest rates lead to appreciation of exchange rate, the costs of

raising interest rates in terms of large recession, decline in investment, corporate failures,

financial system bankruptcies or fragility may outweigh the benefits of an appreciated

exchange rates. In other words, the relative cost of raising interest rates to defend the

currency to letting the exchange rate determined by the market forces may be higher. But an

increase in interest rates in India can lead to exchange rate appreciation without causing any

adverse effects.

It was found that there has been a long-run relationship between the above mentioned

variables. Both call money rate and net intervention have negatively and significantly

influenced the exchange rate, where as the expected rate of inflation differential between the

India and world has not played significant role in the behavior of exchange rate in India.


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Purchasing Power Parity as the Determinant of Exchange Rates:

Evidence from the UK and India - Zakaria Karim

The aim of the empirical work was to set up a test to check whether the Purchasing

Power Parity (PPP) theory holds in the long run or not and also to compare the effect on PPP

when using both long and short horizon data. Most of the previous researches have beenconcluded with mixed results. The research indicates the evidence of purchasing power parity

theory to hold in the long-run.

The main specific objectives are:

To investigate the long-run PPP between UK and India

To investigate the short-run PPP between UK and India

To compare between long and short-run PPP

In this study the monthly dataset from 1970 to 2009 has been used as the main dataset for the

analysis. From this monthly dataset the main objective of this study was to test whether the

purchasing power parity hypothesis holds in the long-run or not. However, at the same time

the quarterly data from 1970 to 2009 and other eight subsample datasets have been used to

compare the result between long and short run PPP and also the impact of the frequency of

the observations.

Monthly (1970-2009) data which implies that PPP holds in the long-run but in case of

quarterly data PPP does not hold. The monthly dataset includes 480 observations whereas the

quarterly dataset comprises of only 160 observations. So, the result obtained from the

analysis indicates that the frequency of the observations in the data series significantly affect

the result although both the datasets are of same time period from 1970 to 2009.

Results obtained from the subsamples are also mixed. Some subsamples showed that PPP

holds in the short-run as well and some showed that PPP does not hold in the short-run. These

subsamples are obtained by dividing the main monthly data in eight equal sub sections of five

years. So, as PPP holds in long-run monthly data and does not hold in some of the short-run

subsamples, it can be inferred that there exists some short-run disequilibrium among the

variables although the main long-run monthly dataset is showing long-run equilibrium

relationship.


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MODULE 2 DESCRIPTION OF DATA

A.INTRODUCTIONFor the analysis in this study a monthly time series from April 1996 to March 2012, a total of

192 observations, is used for the purpose. It is observed that the monetary policy intentions

depicted by the bank rate of the RBI, the short-term and long-term domestic interest

differentials and interest yield differentials, and the rate of change of foreign exchange

reserves and inflation differentials have a significant impact on the monthly average of the

exchange rate between Indian rupee and the US dollar and quite in line with the economic

theory. The type of data used is secondary.

Since the data is time series, there is a common problem of multicollinearity, i.e., the

independent variables are highly correlated with each other. Due to this problem the

coefficients that would be obtained in regression analysis would not reflect true picture.

Therefore, all the values are taken in the form of percentage change keeping previous

months value as base. This significantly reduces the problem of multicollinearity from the

data. The data becomes stationary from non-stationary.

For analyzing the effect of Gross Domestic Product (GDP) on Exchange Rate, Index of

Industrial Production (IIP) has been used as a proxy. The reason is that IIP as a monthly

indicator is widely used for assessing both the current state and the short-term outlook for

GDP since monthly data on GDP are not available. One of the main reasons why the IIP was

considered to be a good proxy for GDP was that the value added by industrial production

represented a substantial share of GDP. In recent decades however the share of services has

grown considerably in most economies and now accounts for at least half of activity in all the

countries. This has led to question whether the relationship between the IIP and GDP cyclescontinues to be close and whether the IIP remains an acceptable proxy for GDP. This is one

limitation of the Data.


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B.DATA SOURCES1. Data related to India, i.e., broad money, yields on 90 days treasury bill and yield on

10 years treasury bill, call rates, bank rates, has been collected from the website of

reserve bank of India under the section of database on Indian economy.

2. Data related to USA, i.e., yields on 90 days Treasury bill and yields on 10 years

Treasury bill and Consumer Price Index (for calculating Inflation), has been collected

from the website of Federal Reserve Bank of St. Louis.

3. Some data related to India, i.e., exchange rate (RS/USD), Consumer Price Index (for

calculating Inflation) and Index of Industrial Production, has been collected from the

website of Federal Reserve Bank of St. Louis under the category of international

data.

C. GRAPHSEXCHANGE RATE AND FOREIGN EXCHANGE RESERVES

The above chart shows high range of volatility in rupee-dollar exchange rates and foreign

exchange reserves over the last sixteen years. Inverse relationship between exchange rate and

foreign exchange reserves is clearly visiblelike in the period of 1997-99, a decrease in

reserves leads to depreciation of home currency and in the period of 2006-08 whereas an

increase in reserves causes a depreciation of home currency. The Correlation Coefficient is -

.447(significant at 1% level)


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EXCHANGE RATE AND BANK RATE

The above chart shows highly fluctuating exchange rate with bank rate where the latter

remains constant for longer time periods as compared to the former. In period of 1997-99,

there exists a negative relation between these two variables as shown by the arrow. But after

the 2004 bank rate became constant with exchange rate still fluctuating. Thus, nothing

meaningful inference can be drawn from above. The Correlation Coefficient is .093

(insignificant).

EXCHANGE RATE AND CALL RATE

The chart above shows highly fluctuating call rate and exchange rate. In 1998, a positive

relationship between the rates is evident, however while in the period of the 2007-08 period

displays a negative relationship between the same. The Correlation Coefficient is .108

(significant at 5% level)


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EXCHANGE RATE AND INFLATION DIFFERENTIAL BETWEEN DOMESTIC AND FOREIGN

COUNTRY

The above chart shows the time series plot of inflation differential and the exchange rate. The

bigger circle indicates the positive relationship between the two variables. An increase in

domestic inflation relative to that of US causes domestic currency depreciation. The other

two circles show the same result but it seems that there is some lag effect of inflation

differential on exchange rate. The Correlation Coefficient is .175 (significant at 5% level).

EXCHANGE RATE AND INDEX OF INDUSTRIAL PRODUCTION

From the above line graph, the negative relation between the index of industrial production

and the exchange rate is clearly visible as shown by the circles drawn. Correlation coefficient

is -.17 (significant at 5% level).


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EXCHANGE RATE AND 90 DAYS YIELD DIFFERENTIAL ON TREASURY BILLS BETWEEN

DOMESTIC AND FOREIGN COUNTRY

The above graph shows the relation between short term yield difference and the exchange

rate. In the time period (marked by smaller circle) inverse relationship can be observed while

in the time period (marked by bigger circle) positive relationship can be observed between

the two variables. The Correlation Coefficient is .201 (significant at 5% level)

EXCHANGE RATE AND 10 YEARS YIELD DIFFERENTIAL ON TREASURY BILLS BETWEENDOMESTIC AND FOREIGN COUNTRY

Same is the case with long term yield difference as it was with the short term yield difference.

Negative relationship can be seen for time period 2004-2005 while positive relationship can

be seen for the time period 2008-10. Correlation coefficient is .202 (significant at 5% level).


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EXCHANGE RATE AND BROAD MONEY

The above graph shows the time series plot of exchange rate and broad money. Correlation

coefficient is -.014 (insignificant).


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MODULE 3 METHODOLOGY

A.MODELTYPE OF REGRESSION MODEL USED: MULTIPLE LINEAR REGRESSION MODEL

(ORDINARY LEAST SQUARES)

FREQUENCY OF DATA: MONTHLY (APRIL 1996 TO MARCH 2012)

TOTAL OBSERVATIONS: 191

EXPLAINED VARIABLE:

GEXR - PERCENTAGE CHANGE IN EXCHANGE RATE FROM THAT OF THE

PREVIOUS MONTH

EXPLANATORY VARIABLES:

GFXR - PERCENTAGE CHANGE IN FORIEGN EXCHANGE RESERVES FROM THAT

OF THE PREVIOUS MONTH

INFD DIFFERENCE BETWEEN INFLATION IN INDIA AND INFLATION IN US.

GIIP - PERCENTAGE CHANGE IN PRODUCTION OF TOTAL INDUSTRY IN INDIA

FROM THAT OF THE PREVIOUS MONTH

STYD SHORT TERM (90 DAYS) YIELD DIFFERENCIAL BETWEEN INDIA AND US

GOVERNMENT BONDS

LTYD LONG TERM (10 YEARS) YIELD DIFFERENCIAL BETWEEN INDIA AND US

GOBERNMENT BONDS

GM3 PERCENTAGE CHANGE IN BROAD MONEY FOR INDIA FROM THAT OF

THE PREVIOUS MONTH

LAGS OF VARIABLES ARE ALSO INCLUDED IN THE MODEL TO CHECK THE

IMPACT OF THERE PREVIOUS MONTH(S) VALUE ON THE VALUE OF EXPLAINED

VARIABLE.

Thus percentage variation in dollar-rupee exchange rate is defined as a function of three

months lag values of exchange rate, call rate and bank rate, including there lag effect for three

months, inflation differential between India and the US, interest yield differentials between

90 days T-bills of India and the US as well as 10-year government securities of India and the

US, money supply in India, foreign exchange reserves and its three lags.


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GRANGER CAUSALITY TEST:

The Granger causality test is a statistical hypothesis test for determining whether one time

series is useful in forecasting another. A time series X is said to Granger-cause Y if it can be

shown through a series of F-tests on lagged values of X (and with lagged values of Y also

included), that those X values provide statistically significant information about values of Y.

The regression of Y with its lagged values is called as a Restricted Regression (RR) and the

regression of Y with its lagged values plus the lagged values of X is called an Unrestricted

Regression (UR). The null hypothesis is that the variable X adds no explanatory power

according to F test. The F statistic is calculated as follows:

F = ((R2

ur R2

rr)/m) / ((1 - R2

ur)/(n-k))

Where m (number of lagged X terms) is numerator degree of freedom and n-k (number of

observations number of parameters estimated in UR.

If the computed F exceeds the critical F value at the chosen level of significance, we reject

our null hypothesis.

F statistics table

GFXR BR CR INFD GIIP STYD LTYD GM3

12.18 2.97 3.05 4.44 7.19 1.01 1.17 0

Critical F value is 2.65 (5% significance level)

NOTE: 3 lags each for every variable in the restricted and unrestricted models are taken.

Since the computed F value for Short Term Yield Difference (STYD), Long Term Yield

Difference (STYD) and Growth Rate of Broad Money (GM3) is less than the critical F value,

the variables are thus excluded from the regression model.

THUS, THE MODEL IS

Y (GEXR) = B0 (INTERCEPT) + B1 (LAG1GEXR) + B2 (LAG2GEXR) + B3

(LAG3GEXR) + B4(GFXR) + B5 (LAG1GFXR) + B6 (LAG2GFXR) + B7 (LAG3GFXR) +

B8(BR) + B9(LAG1BR) + B10(LAG2BR) + B11(LAG3BR) + B12(CR) + B13(LAG1CR) +

B14(LAG2CR) + B15(LAG3CR) + B16(INFD) + B17 (LAG1INFD) + B18 (LAG2INFD) +

B19 (LAG3INFD) + B20 (GIIP) + B21 (LAG1GIIP) + B22 (LAG2GIIP) + B23

(LAG3GIIP)+ Ui (ERROR TERM)


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B.ASSUMPTIONSNO AUTOCORRELATION BETWEEN THE ERROR TERMS U i:

It means that there is no serial correlation between the error term and its lags. For this

DurbinWatson statistic is calculated. It is a test statistic used to detect the presence

of autocorrelation in the residuals from a regression analysis. The value of this comes out to

be 1.91. Our null hypothesis, i.e., no autocorrelation, is accepted here. This can be shown

with the help of a graph:

In the above chart autocorrelation is tested till 30 lags of error term and all the values of

correlation coefficients are insignificant at 5% significance level.

NO PERFECT CORRELATION BETWEEN THE EXPLANATORY VARIABLES:

This is an important assumption of the linear regression model which states that there should

be no perfect collinearity between the explanatory variables, i.e., all the variables should be

uncorrelated with each other. This assumption is important because if there is high correlation

between any two explanatory variables then their respective beta coefficients will not reflect

true effect of that variable on the explained variable. No correlation assumption gives Best

Linear Unbiased Estimators (BLUE). This is called as the assumption of no perfect

multicollinearity.


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Correlation Matrix (Karl Pearson Coefficients Of Correlation)

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

The above matrix satisfies our assumption of no perfect multicollinearity between the

variables.

VARIABLES GEXR GFXR BR CR INFD STYD LTYD GIIP GM3

GEXR 1 -.447** 0.093 .172* .175* .201** .202** -.170* -0.014

0 0.202 0.018 0.015 0.005 0.005 0.019 0.847191 191 191 191 191 191 191 191 191

GFXR 1 -0.008 -0.115 -.197** -.215** -.212** 0.135 .192**

0.91 0.113 0.006 0.003 0.003 0.063 0.008

191 191 191 191 191 191 191 191

BR 1 .324** 0.046 .257** .724** -0.052 -0.008

0 0.525 0 0 0.473 0.907

192 192 191 192 192 191 191

CR 1 0.006 .421** .486** -0.037 0.052

0.936 0 0 0.61 0.475

192 191 192 192 191 191INFD 1 0.103 0.112 -0.08 -0.083

0.158 0.123 0.273 0.252

191 191 191 191 191

STYD 1 .598** -0.136 -0.062

0 0.06 0.392

192 192 191 191

LTYD 1 -0.088 0.012

0.228 0.865

192 191 191

GIIP 1 -0.008

0.911

191 191

GM3 1

191


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ERROR TERMS ARE NORMALLY DISTRIBUTED AND EXPECTED VALUE OF ERROR TERMS IS 0:

This is one of the most important assumptions of the linear regression model that the error

terms or the residuals follow a normal distribution. Properties of normal distribution are -

Mean is 0, standard deviation is 1, skewness is 0 and kurtosis is 3.

In the above chart residuals are plotted with their respective densities. It can be seen that error

terms are approximately normally distributed about mean value very near to 0 and standard

deviation approximately equal to 1. The null hypothesis under the Jarque-Bera test, i.e.,

skewness is 0 and extra kurtosis is 0, is accepted with 5% significance level following a chi-

square distribution with 2 degree of freedom.


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ZERO COVARIANCE BETWEEN ERROR TERMS AND THE INDEPENDENT VARIABLES:

It means that there is no correlation between the explanatory variables and the error terms.

Correlation Matrix (Karl Pearson Coefficients Of Correlation)

NOTE: for scatter plots of residuals and variables see appendix at the end.

EXPLANATORY VARIABLES UNSTANDARDIZED RESIDUALS

LAG1GEXR 0.000

LAG2GEXR 0.000

LAG3GEXR 0.000

GFXR 0.000

LAG1GFXR 0.000

LAG2GFXR 0.000

LAG3GFXR 0.000

BR 0.000

LAG1BR 0.000

LAG2BR 0.000

LAG3BR 0.000

CR 0.000

LAG1CR 0.000

LAG2BR 0.000

LAG3BR 0.000

INFD 0.000

LAG1INFD 0.000

LAG2INFD 0.000

LAG3INFD 0.000

GIIP 0.000

LAG1GIIP 0.000

LAG2GIIP 0.000

LAG3GIIP 0.000


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MODULE 4 RESULTS AND CONCLUSION

A.REGRESSIONRESULTS

OLS, Using Observations 1996:08-2012:03 (N = 188)Dependent Variable: GEXR

VARIABLE

BETA

COEFFICIENT T-STATISTICS P-VALUE SIGNIFICANCE

(Constant) -0.383 -0.587 0.558 INSIGNIFICANT

LAG1GEXR 0.244543 3.1106 0.0022 SIGNIFICANT AT 1% SIG. LEVEL

LAG2GEXR -0.216618 -2.7972 0.00577 SIGNIFICANT AT 1% SIG. LEVEL

LAG3GEXR -0.0206913 -0.2709 0.78682 INSIGNIFICANT

GFXR -0.218414 -5.947 0.00001 SIGNIFICANT AT 1% SIG. LEVEL

LAG1GFXR -0.0265197 -0.6774 0.49909 INSIGNIFICANT

LAG2GFXR -0.00392106 -0.1012 0.91952 INSIGNIFICANT

LAG3GFXR 0.000216193 0.0056 0.99552 INSIGNIFICANT

BR -0.644693 -2.1633 0.03196 SIGNIFICANT AT 5% SIG. LEVEL

LAG1BR 0.802121 1.9992 0.04723 SIGNIFICANT AT 5% SIG. LEVEL

LAG2BR -0.35897 -0.7221 0.47125 INSIGNIFICANT

LAG3BR 0.220715 0.527 0.59889 INSIGNIFICANT

CR 0.0878331 1.8017 0.07341 SIGNIFICANT AT 10% SIG. LEVEL

LAG1CR -0.10913 -2.1414 0.03371 SIGNIFICANT AT 5% SIG. LEVELLAG2CR 0.0888432 1.6462 0.10163 INSIGNIFICANT

LAG3CR 0.0274731 0.6047 0.54618 INSIGNIFICANT

INFD 0.0371463 0.3315 0.74069 INSIGNIFICANT

LAG1INFD 0.0338172 0.2973 0.76664 INSIGNIFICANT

LAG2INFD 0.231718 2.0231 0.04467 SIGNIFICANT AT 5% SIG. LEVEL

LAG3INFD 0.346421 3.165 0.00185 SIGNIFICANT AT 1% SIG. LEVEL

GIIP -0.12486 -2.1374 0.03404 SIGNIFICANT AT 5% SIG. LEVEL

LAG1GIIP -0.0986598 -1.553 0.12234 INSIGNIFICANT

LAG2GIIP -0.199514 -3.1374 0.00202 SIGNIFICANT AT 1% SIG. LEVEL

LAG3GIIP 0.0122399 0.1917 0.84824 INSIGNIFICANT

R-SQUARE = 47.18 %

F-STATISTICS (23,165) = 6.299 P-VALUE (F) = 0.000

NOTE: for testing the significance of various beta coefficients our Null Hypothesis (H0) is

beta coefficient is equal to 0 and Alternate Hypothesis (Ha) is beta coefficient is not equal to

0. Beta coefficients in bold format are significant, i.e., our Null Hypothesis is rejected.


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FITTED AND ACTUAL GEXR

The actual and predicted values are plotted on the graph for a period of 16 years.

From above plot it can be inferred that the predicted values of GXR does not follow the

actual plot for the period of 1996 to 2004. But after 2004, the predicted and actual values ofGEXR approximately move together. This shows that the above regression model holds good

for the period after the year 2004.


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B.CONCLUSIONS

The above regression shows that almost 47 per cent variations in the dollar-rupee exchange

rates are due to the variables included in the model. However, money supply and the interest

rate differences between India and the US are not so significant determinants of the same.

The first period lag effect of exchange rate is positive, indicating contribution of the past

changes in building up the anticipation of the economic agents as far as the exchange rate

changes are concerned. But at the same time, the effect of 1st period lag is set off by the

effect of second period lag. Since the positive beta coefficient of 1st period lag is more than

that of the 2nd period lag, the domestic currency depreciates.

Rate of change in the foreign exchange reserves is a significant determinant of exchange rate.

This reflects a direct and negative supply side impact on the price of the foreign currency in

terms of the domestic currency. The result thus obtained is in accordance with the

macroeconomic theory. However, all the three lags of foreign exchange reserves that are

introduced in the model are insignificant.

The bank rate and 1st period lag of call rate have negative impact on rupees per dollar

exchange rate, indicating appreciation of the domestic currency with an increase in the rates.This shows the RBI policy, as reflected by this, which leads economic agents to anticipate a

continuation of the policy in future. That is, a tighter monetary policy regime leads to

anticipation for a further tightening of monetary policy regime in future, which results in

appreciation of rupees against dollar. The bank rate is an indicator of long-term monetary

policy intentions of the RBI. However, correction mechanism sets in as shown by the positive

beta coefficient of 1st period lag of bank rate, indicating that positive impact of tighter

monetary regime have settled in, and the exchange rate will depreciate as a consequence of a

rise in the bank rate and subsequently easing out of the access liquidity.

The negative value of the coefficient of changes in the bank rate indicates that an increase in

the bank rate leads to an appreciation of the domestic currency. This is because the economic

agents form confirmatory anticipation regarding the future bank rate policy to control high

rates of inflation and contain the growth of money supply in the economy. Not only the

anticipation effect, but also the immediate cost of the domestic investment to increase since

the rise in bank rate is followed by a rise in all interest rates across the board. As theinvestment funds flow tightens in the economy, the value of the domestic currency

appreciates against the foreign currency.


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The variables of specific interest here are the inflation differential between India and US

economies and the growth rate of production of total industry in India.

The insignificant beta coefficient of the inflation differential shows that there is no immediate

impact of it on the rupees per dollar exchange rate.

Similarly the 1st period lag effect is also insignificant on exchange rate.

The effects of inflation differential come from the 2nd period and the 3rd period lags.

Hence, the relative purchasing power parity is proved here. A rise in the domestic prices in

relation to the foreign prices makes the foreign goods cheaper than the domestic goods which

lead to increase in imports and depreciation of domestic currency. However, a rise in inflation

now will have an effect on exchange rate after 2-3 months.

An interesting observation here is that a 1% increase in the inflation differential leads to a

less than 1% depreciation of the domestic currency.

The growth rate of production of total industry in India has a negative and significant impact

on the exchange rate. This is due to changes in the supply and prices at domestic level. An

increase in the production in domestic economy (keeping the demand constant) raises the

supply of commodities which results in lowering of the domestic prices due to competitive

supply. The lower prices attract the foreign demand and lead to creation of more demand of

domestic currency in the foreign exchange market. An increase in demand of currency

appreciates it.

This also follows the Marshall-Lerner condition which states that an increase in exchange

rate causes total domestic product to fall in the short run. Thus, the negative beta coefficients

of growth rate of production and its lags follow the theory.


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However, both long-term interest yield differential (difference between yield on 10 year

Treasury bill in India and yield on 10 year Treasury bill in US and the short-term interest

yield differential (difference between yield on 90 days Treasury bill in India and yield on 90

days Treasury bill in US) both are not included in the model. This is due to their insignificant

contribution to the explanation of the exchange rate fluctuations shown by Granger Causality

test. According to the theory, both the variables should have negative impact on the exchange

rate because an increase in the interest yield differentials implies that comparative return on

investment in India is higher than in the US. This results in greater investment inflow in the

Indian economy, leading to appreciation of rupee against the US dollar. This also shows that

higher returns are associated with higher risks also it may be the case that foreigners rather

buy shares instead of treasury bonds. If this were the strongest component of currency

demand, then an increase of interest rate may even lead to the opposite results, since an

increase of interest rate quite often depresses the stock market, leading to share sales by

foreigners.

Growth of money supply in India, in isolation, is not a significant determinant of exchange

rate between the rupee and the dollar according causality test. This may be due to the fact that

the impact of growth of money supply on the national income is not taken into consideration.

Similarly, a comparison between the rate of change on money supply in India and that in the

US may have greater explanatory power in exchange rate determination.


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REFERENCES

1. Gujarati, Damodar N., Basic Econometrics 4th Edition, Tata McGraw Hill.

2. Karmel, Peter and Polasek, M., Applied Statistics for Economists.

3. SPSS manual.

4. Cottrell, Allin, Gretl Manual- Gnu Regression, Econometrics and Time-series

Library.

5. Bhanumurthy, N.R., Macroeconomic Fundamentals and Exchange Rate Dynamics in

India: Some Survey Results.

6. Dash, Pradyumna, Interest, Inflation, Bank Intervention and Exchange Rates.

7. Karim, Zakaria, Purchasing Power Parity as the Determinant of Exchange Rates:

Evidence from the UK and India, School of Economics and Finance, Queen Mary

University of London.

8. Blanchard, Oliver, Macroeconomics.

9. Salvatore, Dominick, International Economics 8th Edition, wiley publications.

10.Suthar, Mita H., Determinants of Exchange Rate in India.

11.Wikipedia-the encyclopedia, www.wikipedia.org

12.Kalra, SHOWBHIK, Note on Determinants of Foreign Exchange Rates.

13.Abhishek Kumars Blog, Falling Indian Rupee, abhishekkumar.typepad.com


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APPENDIX

Residual plot with foreign exchange reserves Residual plot with bank rate

Residual plot with Call Rate Residual plot with Inflation Differential


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Residual plot with IIP Residual plot with Broad Money

Residual plot with Long Term Yield Residual plot with Short Term Yield

Differential Differential


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B.B.E. (Semester-V)

Evaluation of Project Report

Paper-502: Computational Techniques

EVALUATION SHEET

Name of Candidate: __________________________ Roll No:

S. No Basis of Examining Candidate M. marks. Award

1 Analysis of topic Objectives and

Hypothesis

10

2 Scope, coverage and Review of

Literature

10

3 Quality of Research Methodology

Applied

10

4 Interpretation of Statistical Results-content and depth

10

5 Ability to highlight limitations and

suggestions for further research

10

Total 50

Note: 40% of these marks shall be assigned to each of the participating student

of this Group Project, as per Para 1(b) of Project Report Evaluation, given

above.

Remarks:

(External Examiner) (Internal Examiner)Code:_______ Code: ________

(Note: This evaluation Performa should be pasted inside each Project Report for

use by Internal and external examiner.)


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Evaluation Sheet to be used for Individual Student of Group Project

University

Roll no.

Name of the

candidate

Marks

from

project

40%

Questions on any

part of Project-

30%

Questions on

specific Module-3

Marks

Marks:

50

1 2 3 4 5 6

(External Examiner) (Internal Examiner)

Code: ________ Code: _______

exchange rate and its determinants in india

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