Relationship Between Stock Price and Exchange Rate in European Countrie1 234

RELATIONSHIP BETWEEN STOCK PRICE AND EXCHANGE RATE IN EUROPEAN

COUNTRIESMEHAK ZAINAB

BENAASH AKRAM

Department of Commerce, University of Sargodha

Abstract:

This study aims at exploring the relationship between STOCK PRICE AND EXCHANGE RATE in four European Countries (Germany, France, United Kingdom, Norway) .Data was taken from JANUARY 2002 to OCTOBER 2007 on Monthly basis and we analyzed the data by using Co integration analysis. For Co integration it was necessary that data should be stationary. For this purpose Unit Root Test was used and both series were found to be integrated at first Difference. Co integration analysis indicated that there exists co integration between Stock prices and Exchange Rate for all four European Countries.

Keywords: Stock Price, Exchange Rate, European Countries

INTRODUCTION:

Mutual relations between foreign exchange rate and stock market prices have gained much attention of Researchers since the beginning of 1990s.The last quarter of the century has showed significant changes in International financial system like Foreign exchange restrictions, emergence of new capital market or adoption of more flexible exchange rate arrangements. All above mentioned features have broadened the variety of investment opportunities but, on other side, they have also enhanced the volatility of exchange rate and added the risk level to the overall investment decision and portfolio diversification process. Therefore the examining the relationship between exchange rate and stock market prices has become more complex and has gained more research interest than before.

This research is made to study the long run equilibrium and short run dynamic relationship between exchange rate and stock prices in four European Countries (GERMANY, FRANCE, NORWAY, UNITED KINGDOM).A positive relation between the exchanges rate and stock prices may result as the real interest increases, capital inflow increases and the exchange rate falls. While the theory of arbitrage suggests that a higher real interest decreases the present value of firms, future cash flows and then results fall in stock prices and vice.

There are a number of existing studies that attempt to determine the interdependence among exchanges rate and stock market prices, however the results are not uniform (Ibrahim, 2000). Some studies found the positive effects on exchange rates on stock markets (Aggarwal, 1981), while others found negative effects (Soenen and Henningar, 1988). Other studies concluded that the exchange rate changes have no significant impact on the stock market (Solnik, 1984). Thus, the existing literature provides mixed results when analyzing the relationship between exchange rates and stock market price.

In this topic I am trying to determine whether a dynamic relationship between exchange rate and stock market price in European countries (GERMANY, FRANCE, NORWAY, UK) exists in long run and short run.

Literature Review:

Aggarwal,(1981) examined the influence of exchange rate on U.S stock prices .He used monthly data from a period of 1974-1978 .Results revealed that stock price and exchange rates are positively correlated

P.Jorion,(1990) conducted research on exchange rate exposure of U.S multinationals to foreign currency risks .They measured change in value of firm on change in exchange rate .The results revealed that co. movement between stock return and value of dollar is found to be positively related to percentage of foreign operations of U.S multinationals.

Clive W. Granger et al,(2000) examined a bivariate causality between stock price and exchange rate they developed co. integration model to determine the appropriate Granger relationship between price and exchange rate using recent flu data. They found that from South Korea are in agreement with traditional approach. That is exchange rate leads to stock price. On the other hand data of Philippine suggests the results expected under portfolio approach. That is stock price leads to exchange rate with positive correlation. They examined that data from Hong Kong ,Malaysia indicate strong feedback relation whereas Indonesia and Japan fails to reveal any recognizable pattern

Chien.Chung Niew and Chien Lee,(2002) conducted a research on dynamic relationship between stock prices and exchange rate for G-7 countries .They found that short run significant relationship has been found for 1 day in certain G-7 countries in addition they found that stock price and value of dollar cannot be dependent on when predicting future relationship in U.S either in short or long run

Yutaka Kurihara ,(2006) selects the period from March 2001 to September 2005 to examine the relationship between macro. economic variables and daily stock prices in Japan .Results I indicate that the exchange rate influences the Japanese stock prices.

Charles et al,(2006) investigate the relationship between stock prices and exchange rate movements in seven African countries .He used autoregressive co. integration and impulse response analysis to determine the long run and short run linkage between stock prices and exchange rate .Findings show that there is long run relationship between stock price and exchange rate in Tunisai .Impulse response analysis for other countries show that stock returns in Ghana , Kenya, Nigeria reduces when induces by exchange rates shocks but increase in Egypt and South Africa.

Donotas Pilinkus , (2008) analyze the relationship between group of macro. Economic variables and the Lithuanian stock market index OMX. The aim of this study is to investigate whether stock price may serves as a leading indicator for macro Economic variables or a group of macro Economic variables may serve as leading indicator for stock returns .Granger causality test has been implied from December 1999 to March 2008.Results showed that some macro. Economic variables lead market stock returns and some lead by OMX Vilnius index.

Baharom et al, (2008) examined causation analysis between stock price and exchange rate by using Japanese (1991) co. integration method. They divided the period in two sub periods albeit pre crisis and posy crisis. The results revealed no long run relationship between stock price and exchange rate for both countries.

Md. Lutfar Rahman and Jashim Uddin (2009) investigated the dynamic relationship between stock prices and Exchange Rates in three emerging countries of South Asia as Pakistan, India and Bangladesh.They considered monthly values of Exchange rate and Stock prices for a period of January 2003 to June 2008 to conduct this study.They have applied Johansen procedure to test for the possibility of Cointegration relationship.results shows that there is no cointegration relationship between stick price and exchange rates.

Shehu Usman Rano Aliyu (2009) examined the short run and long run interaction between stock prices and exchange rate in Nigeria based on a sample from 1st January, 2001 to 31st

December, 2008. They have applied bivariate cointegration and Granger Causality test for examing the interactions between stock prices and Exchange rates. Cointegration implies the existance of long run relationship between stock prices and Exchange rate.

Oguzhan Aydemir and Erdal Demirhan (2009) chooses the period from February 2001 to January 2008 to investigated the relationship between stock prices and exchange rates about Turkey.The reason of selecting this period is that exchange rate regime is determined as floating in this study.The results of empirical study show that there is bi-directional causal relationship between exchange rate and all stock market indices.

Nadeem Sohail, Zakir Hussain (2009) examined the long run and short run relationship between Lahore Stock exchange and macroeconomic variables in Pakistan.The variables induces in this study were Consumer price index, real effective exchange rate, three months treasury bill rate, Industrial production index and LSE 25 index for the period of December 2002 to June 2008. Cointegration test was used to identify the relationship between these variables,Results showed that inflation had negative impact on Stock prices in the long run while industrial production index, real effective exchange rates and money supply affected stock returns possitively however three month treasury bill rate showed insignificant positive impact on stock returns on the long run.

Manish kumar (2009) choosed the period January 1998 to August 2009 to examined the bivariate linear and non-linear Cointegration betweeen stock prices and exchange rate for India. The study uses the unit root and Cointegration tests to investigate the long run relationship between stock price and exchange rate. The empirical evidence show that there is no long run relationship while there is bi-directional linear and non-linear causality between stock price and exchange rate.

Noel Dilrukshan Richards et al.(2009) examined the interaction between Australian stock prices and Australian-USD exchange rate from January 2003 to June 2006.During the period of study, value of stock market price increased by two-third and Australian dollar exchange rate appreciated by almost one-third.The empirical analysis employed provide the evidence of positive cointegration between these variables by using Granger causality test.

Dharmindra Singh (2010) has been used monthly data from April 1995 to March 2009. Attempts has been made to explore the causal stock market indices i-e BSE and three macroeconomic variables like exchange rate, industrial production index and wholesale price index(WPI) using correlation, unit root test and Granger causality test. The cointegration implied the existance of long run relationship between stock price and exchange rate.

Mohamed Essaied and Abdelkad Trifi (2011) investigated the relationship between interest rate, exchange rate and stock price by applying Wavlet correlation and Cross correlation on data over the period from January 1990 to December 2008.They analyzed the association as well as relationship between these series at different time scales.The results revealed that exchange rate return and stock index return have a bi-directional relationship in this period at longer horizon.They found that relationship between interest rate and exchange rate is not significantly zero at all scale where as the relationship between interest rate and stock index returns is significantly zero, different from zero only at highest level.

Chia-Hao-Lee et al.(2011) investigated the interaction between stock price & exchange rate & explore their dynamic correlation influend by the stock market volatility.They use weekly data from Indonesia, Korea, Malaysia, Taiwan,Philippines and Thailand for the period of 2000 to 2008. The empirical results that there are significant spillovers from stock market to foreign exchange market for Indonesia, Koria, Malaysia, Thailand and Taiwan. Furthermore , the correlation between stock and Foreign exchange markets becomes higher when stock market volatility increases in Asian emerging markets except Philippines.

Dr Naliniprava Tripathy (2011) examined the market efficiency and causal relationship between selected macroeconomic variables and Indian stock market during the period from January 2005 to February 2011 by using Ljung-Box Q test, Breusch-Godfrey LM test, Unit Root test and Granger causality test. The study confirms the presence of autocorrelation in Indian stock market and macroeconomic variables.

Data Description and Methodology:This study includes stock prices indexes and exchange rate for the period of January 2002 to 2007 October on the basis of monthly data for four European Countries.The rate of return is calculated by using the following model

Return = Rt = ln(Pt / Pt- 1)

Where

Rt = Return for Given Period‘t’Pt = Price at closing timePt-1 = Price at the opening timeln = Natural LogThere are several methods for testing the flow of information and co-movement of prices in stock markets across the countries. In this study the emphasis is given to test the inter-market relationship among the stock market in Pakistan with that of equity markets of developed world, via; (i) Descriptive statistics (ii) Correlation matrix, (iii) Co integration tests, and (iv) Granger causality test.

HYPOTHESIS

Following hypothesis of the study are confirmed by applying the above explainedMethodologies.

Hypothesis: 1

H1 There is a significant relationship between German stock market price and its exchange rate.

HO There is no significant relationship between German stock market price and its exchange rate.

Hypothesis: 2

H1 There is a significant relationship between France stock market price and its exchange rate.

Ho There is no significant relationship between France stock market price and its exchange rate.

Hypothesis: 3

H1 There is a significant relationship between Norway stock market price and its exchange rate.

Ho There is no significant relationship between Norway stock market price and its exchange rate.

Hypothesis: 4

H1 There is a significant relationship between Uk stock market price and its exchange rate.

Ho There is no significant relationship between Uk stock market price and its exchange rate.

Empirical Results:

1. GERMANY

TABLE 1.DESCRIPTIVE STATISTICS

GERMANY EXCHANGE RATE

GERMANY STOCK RETURNS

Mean 1.000411 1.00078 Median 1.000387 1.002214 Maximum 1.006094 1.02486 Minimum 0.99674 0.964314 Std. Dev. 0.001553 0.008216 Skewness 0.650638 -1.21051 Kurtosis 5.148245 8.445982 Jarque-Bera 18.1363 102.1201 Probability 0.000115 0 Sum 69.02837 69.05381 Sum Sq. Dev. 0.000164 0.004591

Observations 69 69

Descriptive Statistics is used to analyze the behavior of the stock returns and exchange rate . Descriptive statistics employed on the returns and exchange rate showed that Germany has an average exchange rate of 100.04% with standard deviation of .0015 percent and average return of 100.078 % with standard deviation of .008 %. The standard deviation of stock price of Germany during selected period is greater than exchange rate. So stock price shows higher level of risk than exchange rate.

TABLE 2.CORRELATION

GERMANY EXCHANGE RATE

GERMANY STOCK PRICES

GERMANY EXCHANGE RATE 1 -0.13658GERMANY STOCK RETURNS -0.13658096 1

Table 2 presents the correlation results for the Germany .It is found that there exists no significant correlation among the stock prices and exchangr rate .. Stock price has very weak negative correlation with the exchange rate. From the results of Correlation it is clear that there exist no positive correlation . Correlation Analysis is considered a weak technique to explore the integration among the markets because it only discusses the relationship and does not considers the lead lag relationship. So, Cointegration and Granger Causality are used to further investigate this issue.

TABLE3.VAR

Sample: 1 70Included observations: 58 Lag LogL LR FPE AIC SC HQ

0 165.1865 NA 1.23E-05 -5.627122 -5.556072 -5.599447

1 305.3599 265.846 1.13E-07 -10.32275 -10.10960*

-10.23973*

2 306.0915 1.337143 1.26E-07 -10.21005 -9.854803 -10.071683 306.2143 0.216025 1.45E-07 -10.07636 -9.579008 -9.882634 308.4308 3.745097 1.54E-07 -10.01486 -9.375408 -9.7657785 311.0167 4.190831 1.63E-07 -9.966092 -9.184545 -9.6616636 313.7194 4.193915 1.71E-07 -9.921359 -8.997712 -9.561587 328.1184 21.35023 1.20E-07 -10.27994 -9.214198 -9.8648158 336.1733 11.38791 1.06E-07 -10.41977 -9.211922 -9.9492879 338.421 3.022772 1.14E-07 -10.35934 -9.009398 -9.833513

10 341.6128 4.072283 1.20E-07 -10.33147 -8.839429 -9.75029311 346.0807 5.392393 1.21E-07 -10.34761 -8.713467 -9.711079

12 363.9774 20.36522* 7.68e-08* -10.82681* -9.050564 -10.13493

* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion

Lag selection is a pre-requisite in order to employ co-integration test. To estimate Johansen and Julius (1991) unrestricted VAR is estimated. Both Akaike information criterion and Schwarz criterion are found minimum at one lag. So one month lag is appropriate lag length.

TABLE 4.UNIT ROOT TEST

ADF LEVEL ADF FIRST DIFF

PP LEVEL PP FIRST DIFF

GERMANY EXCHANGE RATEGERMANY STOCK PRICES

-2.61604-0.01818

-5.65321-4.83568

-2.20328-0.03266

-7.83084-8.13417

CRITICAL LEVEL1%5%10%

-3.53003-2.90485-2.58991

-3.53159-2.90552-2.59026

-3.52852-2.9042-2.58956

-3.53003-2.90485-2.58991

To run co-integration test it is necessary for the data to be stationary of same order. Above tests ensure that this data is non-stationary at level but becomes stationary at first difference. Data stationary is tested through Augmented Dicky Fuller and Phillip Perron Tests as the later is not much strict in nature as is ADF test and both tests confirmed similar results (Dickey & Fuller ,1981).Data is stationery of same order so we can test co-integration among the stock price and exchange rate.

TABLE 5 .CO-INTEGRATION

Unrestricted Co-integration Rank Test (Trace)

Hypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**None * 0.2158 16.82964 15.49471 0.0313At most 1 0.004394 0.299437 3.841466 0.5842Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**None * 0.2158 16.5302 14.2646 0.0215At most 1 0.004394 0.299437 3.841466 0.5842 Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Trace test and Max-Eigen value test both represent the same results that Co-integration exists. Co-integration examines the co-movement among the time series. It does not identify the lead lag relationship. Granger Causality is used to determine the lead lag relationship.

TABLE 6.GRANGER CAUSALITY TESTS

Pairwise Granger Causality TestsDate: 06/14/12 Time: 01:27Sample: 1 69Lags: 1

Null Hypothesis: Obs F-Statistic Prob.

SER02 does not Granger Cause SER01 68 7.90E-05 0.993 SER01 does not Granger Cause SER02 0.426 0.5163

Granger representation Theorem says that if co-integration is found betweentwo time series then granger causality must exist from at least one direction and the results in our research indicates that co integration exists.

TABLE 7.IMPULSE RESPONSE FUNCTION

-.0010

-.0005

.0000

.0005

.0010

.0015

.0020

1 2 3 4 5 6 7 8 9 10

Response of SER01 to SER01

-.0010

-.0005

.0000

.0005

.0010

.0015

.0020

1 2 3 4 5 6 7 8 9 10


-.004

.000

.004

.008

1 2 3 4 5 6 7 8 9 10


-.004

.000

.004

.008

1 2 3 4 5 6 7 8 9 10


Response to Cholesky One S.D. Innovations

Impulse response function indicates that exchange rate and stock price shows greater deviation due to their own effects . The deviation in exchange rate due to stock price and deviation in stock price due to exchange rate is very low.

TABLE 8.VARIANCE DECOMPOSITION

Variance Decomposition of SER01:

Period S.E.GERMANY EXCHANGE RATE


1 0.00166 100 02 0.001678 99.36642 0.6335813 0.001699 97.67038 2.3296244 0.001802 87.62591 12.374095 0.00183 86.28706 13.712946 0.001876 84.08081 15.919197 0.001925 81.85908 18.140928 0.001962 80.27838 19.721629 0.002004 78.42982 21.57018

10 0.002045 76.7643 23.2357


Period S.E.GERMANY EXCHANGE RATE


1 0.008861 3.929958 96.070042 0.009062 7.023197 92.97683 0.00982 12.28366 87.716344 0.010862 17.34012 82.659885 0.011301 19.203 80.7976 0.011966 20.90558 79.094427 0.012589 22.31591 77.684098 0.013094 23.54565 76.454359 0.013651 24.60844 75.39156

10 0.014165 25.51674 74.48326 Cholesky Ordering: SER01 SER02

Variance decomposition can be defined as decomposition of variance due to changes in same series or other series in previous periods. Table 1 shows the variance decomposition of exchange rate with itself and with stock prices. The maximum decomposition of exchange rate with itself is 100% and with stock price is 23.23%. Table 2 shows the variance decomposition of stock price with itself and with exchange rates. The maximum decomposition of stock price with itself is 96% and with exchange rate

2-FRANCE

TABLE 1 :DESCRIPTIVE STATISTICS

EXCHANGE RATE STOCK RETURNS Mean 1.000358 1.000478 Median 1.00035 1.001603 Maximum 1.004943 1.015876 Minimum 0.997356 0.976328 Std. Dev. 0.001266 0.006079 Skewness 0.759491 -0.968401

Kurtosis 5.053991 6.366161 Jarque-Bera 18.76279 43.36144 Probability 0.000084 0 Sum 69.02467 69.03295 Sum Sq. Dev. 0.000109 0.002513 Observations 69 69

Descriptive Statistics is used to analyze the behavior of the stock returns and exchange rate . Descriptive statistics employed on the returns and exchange rate showed that France has an average exchange rate of 100.0358% percent with standard deviation of .126 percent and average return of 100.0478 % with standard deviation of .6079 %. The standard deviation of stock price of France during selected period is greater than exchange rate. So stock price shows higher level of risk than exchange rate.

TABLE 2 :CORRELATION

EXCHANGE RATE STOCK RETURNSEXCHANGE RATE 1 -0.108111237STOCK PRICES -0.108111237 1

(5% LEVEL OF SIGNIFICANCE)

Table 2 presents the correlation results for the France .ItIs found that there exists no significant correlation among the stock prices and exchangr rate .. Stock price has very weak negative correlation with the exchange rate. From the results of Correlation it is clear that there exist no positive correlation among the returns of stock market and exchange rate of france.. . Correlation Analysis is considered a weak technique to explore the integration among the markets because it only discusses the relationship and does not considers the lead lag relationship. So, Cointegration and Granger Causality are used to further investigate this issue.

TABLE 3:VAR

Sample: 1 70Included observations: 58 Lag LogL LR FPE AIC SC HQ

0 190.8542 NA 5.09E-06 -6.512213 -6.441164 -6.48454

1 333.7388 270.98814.24E-08 -11.30134 -11.08819*

-11.21831*

2 334.3513 1.1192974.76E-08 -11.18453 -10.82928 -11.0462

3 334.4217 0.1237975.46E-08 -11.04902 -10.55168 -10.8553

4 338.1612 6.3184685.53E-08 -11.04004 -10.40059 -10.791

5 341.0463 4.6758665.77E-08 -11.0016 -10.22005 -10.6972

6 345.6016 7.0686165.70E-08 -11.02075 -10.0971 -10.661

7 357.4261 17.532824.39E-08 -11.29055 -10.22481 -10.8754

8 361.7495 6.1124564.39E-08 -11.30171 -10.09386 -10.8312

9 363.2757 2.0524774.85E-08 -11.2164 -9.866459 -10.6906

10 365.7743 3.1878735.20E-08 -11.16463 -9.672587 -10.5835

11 369.7437 4.7906655.33E-08 -11.16358 -9.529433 -10.5271

12 383.503 15.65706*

3.92e-08*

-11.50010* -9.723859 -10.8082



TABLE 4 : UNIT ROOT TEST

ADF LEVEL ADF FIRST DIFF.

PP LEVEL PP FIRST DIFF.

SER01SER02

-2.36953-0.35848

-5.65524-4.92117

-1.95991-0.33303

-7.6777-7.75068

CRITICAL VALUES1%5%10%

-3.53003-2.90485-2.58991

-3.53159-2.90552-2.59026

-3.52852-2.9042-2.58956

-3.53003-2.90485-2.58991

.

To run co-integration test it is necessary for the data to be stationary of same order. Above tests ensure that this data is non-stationary at level but becomes stationary at first difference. Data stationary is tested through Augmented Dicky Fuller and Phillip Perron Tests as the later is not thatmuch strict in nature as is ADF test and both tests confirmed similar results (Dickey & Fuller ,1981).Data is stationery of same order so we can test co-integration among the stock price and exchange rate.

TABLE 5: COINTERGRATION

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue StatisticCritical Value Prob.**

None * 0.201743 15.8535 15.49471 0.0441

At most 1 0.007785 0.53146 3.841466 0.466 Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

HypothesizedMax-Eigen 0.05


None * 0.201743 15.32204 14.2646 0.0339

At most 1 0.007785 0.53146 3.841466 0.466 Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Trace test and Max-Eigen value test both represent the same results that Co.integration exists. Co-integration examines the co-movement among the time series. It does not identify the lead lag relationship. Granger Causality is used to determine the lead lag relationship.

TABLE 6: GRANGER CAUSALITY TEST

Pairwise Granger Causality TestsDate: 06/13/12 Time: 13:50Sample: 1 69Lags: 1


SER02 does not Granger Cause SER01 68 0.00892 0.925 SER01 does not Granger Cause SER02 0.96462 0.3297


TABLE 7 : IMPULSE RESPONSE FUNCTION

-.0010

-.0005

.0000

.0005

.0010

.0015

1 2 3 4 5 6 7 8 9 10


-.0010

-.0005

.0000

.0005

.0010

.0015

1 2 3 4 5 6 7 8 9 10


-.004

-.002

.000

.002

.004

.006

.008

1 2 3 4 5 6 7 8 9 10


-.004

-.002

.000

.002

.004

.006

.008

1 2 3 4 5 6 7 8 9 10




TABLE 8 :VARIANCE DECOMPOSITION

Variance Decomposition of SER01: Period S.E. SER01 SER021 0.001375 100 02 0.001408 98.6374 1.3626013 0.001438 96.15506 3.8449354 0.001549 85.08501 14.914995 0.001595 83.26283 16.737176 0.001652 81.27569 18.724317 0.001711 79.49824 20.501768 0.00176 77.83734 22.162669 0.001811 76.18583 23.81417

10 0.001861 74.70535 25.29465

Variance Decomposition of SER02: Period S.E. SER01 SER02

1 0.006639 4.105839 95.894162 0.006918 9.79823 90.201773 0.007527 17.33428 82.665724 0.008231 24.2956 75.70445 0.008655 26.72863 73.271376 0.009142 28.77922 71.220787 0.009618 30.51246 69.487548 0.010039 32.14561 67.854399 0.010463 33.5417 66.458310 0.010865 34.71668 65.28332

Cholesky Ordering: SER01 SER02

Variance decomposition can be defined as decomposition of variance due to changes in same series or other series in previous periods. Table 1 shows the variance decomposition of exchange rate with itself and with stock prices. The maximum decomposition of exchange rate with itself is 100% and with stock price is 25.29%. Table 2 shows the variance decomposition of stock price with itself and with exchange rates. The maximum decomposition of stock price with itself is 95.89% and with exchange rate is 34.71

3- NORWAY

TABLE 1: DESCRIPTIVE STATISTICS

NORWAY EXCHANGE RATE NORWAY STOCK RETURNS Mean 1.000428 1.003443 Median 1.000677 1.005763 Maximum 1.009426 1.021404 Minimum 0.991238 0.967752 Std. Dev. 0.003528 0.010417 Skewness -0.404632 -0.704864 Kurtosis 3.156133 3.551197 Jarque-Bera 1.952951 6.587063 Probability 0.376636 0.037123

Sum 69.02952 69.23758 Sum Sq. Dev. 0.000847 0.007379 Observations 69 69

Descriptive Statistics is used to analyze the behavior of the stock returns and exchange rate . Descriptive statistics employed on the returns and exchange rate showed that Norway has an average exchange rate of 100.042% percent with standard deviation of .003528 percent and average return of 100.34 % with standard deviation of .01 %. The standard deviation of stock return of Norway during selected period is greater than exchange rate. So stock returns shows higher level of risk than exchange rate.

TABLE 2: CORRELATION

NORWAY EXCHANGE RATE NORWAY STOCK RETURNNORWAY EXCHANGE RATE 1 -0.119162058NORWAY STOCK PRICE -0.119162058 1

Table 2 presents the correlation results for the Norway .It Is found that there exists no significant correlation among the stock prices and exchangr rate .. Stock price has very weak negative correlation with the exchange rate. From the results of Correlation it is clear that there exist no positive correlation among the returns of stock market and exchange rate of Norway. . Correlation Analysis is considered a weak technique to explore the integration among the markets because it only discusses the relationship and does not considers the lead lag relationship. So, Cointegration and Granger Causality are used to further investigate this issue.

TABLE 3.VAR

Sample: 1 70Included observations: 58

Lag LogL LR FPE AIC SC HQ

0 82.24417 NA 0.000215 -2.76704 -2.695991 -2.73937

1 253.4789 324.7554* 6.74e-07*

-8.533754*

-8.320604*

-8.450728*

2 253.9763 0.909143 7.62E-07 -8.412976 -8.057727 -8.27463 255.4541 2.598946 8.32E-07 -8.326005 -7.828657 -8.132284 256.1518 1.178788 9.35E-07 -8.212131 -7.572683 -7.96305

5 258.2989 3.479809 1.00E-06 -8.148238 -7.366691 -7.843816 258.9696 1.040724 1.13E-06 -8.033434 -7.109788 -7.673667 261.4455 3.671198 1.20E-06 -7.98088 -6.915134 -7.565758 267.8924 9.114578 1.12E-06 -8.065256 -6.85741 -7.594789 272.9322 6.777649 1.09E-06 -8.101111 -6.751165 -7.57528

10 275.7229 3.560544 1.16E-06 -8.059411 -6.567366 -7.4782311 283.554 9.451379 1.04E-06 -8.191519 -6.557374 -7.5549912 287.8102 4.843246 1.06E-06 -8.200353 -6.424109 -7.50847



TABLE 4.UNIT ROOT TEST

ADF LEVEL ADF FIRST DIFF

PP LEVEL PP FIRST DIFF

NORWAY EXCHANGE RATENORWAY STOCK PRICE

-2.100910.298233

-5.56788-5.18609

-2.055510.420287

-7.17677-6.75394

CRITICAL VALUE1%5%10%

-3.53003-2.90485-2.58991

-3.53159-2.90552-2.59026

-3.52852-2.9042-2.58956

-3.53003-2.90485-2.58991

To run co-integration test it is necessary for the data to be stationary of same order. Above tests ensure that this data is non-stationary at level but becomes stationary at first difference. Data stationary is tested through Augmented Dicky Fuller and Phillip Perron Tests as the later is not that much strict in nature as is ADF test and both tests confirmed similar results (DickeyFuller,1981).Data is stationery of same order so we can test co-integration among the stock price and exchange rate.

TABLE 5.COINTEGRATION


Hypothesized Trace 0.05


None 0.138069 10.11094 15.49471 0.2723At most 1 0.00011 0.007508 3.841466 0.9305 Trace test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values


HypothesizedMax-Eigen 0.05

No. of CE(s) Eigenvalue StatisticCritical value Prob.**

None 0.138069At most 1 0.00011 10.10343 14.2646 0.2052

0.007508 3.841466 0.9305 Max-eigenvalue test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Trace test and Max-Eigen value test both represent the same results that Co.integration exists. Co-integration examines the co-movement among the time series. It does not identify the lead lag relationship. Granger Causality is used to determine the lead lag relationship.

TABLE 6.GRANGER CAUSALITY TEST

Pairwise Granger Causality TestsDate: 06/14/12 Time: 02:01Sample: 1 69Lags: 1 Null Hypothesis: Obs F-Statistic Prob. SER02 does not Granger Cause SER01 68 0.00411 0.9491

SER01 does not Granger Cause SER02 1.77429 0.1875


7.IMPULSE RESPONSE FUNCTION

-.001

.000

.001

.002

.003

.004

1 2 3 4 5 6 7 8 9 10


-.001

.000

.001

.002

.003

.004

1 2 3 4 5 6 7 8 9 10


-.008

-.004

.000

.004

.008

.012

1 2 3 4 5 6 7 8 9 10


-.008

-.004

.000

.004

.008

.012

1 2 3 4 5 6 7 8 9 10






1 0.003836 100 02 0.003973 97.33022 2.6697843 0.004078 93.02285 6.9771484 0.004173 89.52136 10.478645 0.004273 87.60468 12.395326 0.004381 85.7914 14.20867 0.004485 83.92359 16.076418 0.004582 82.22365 17.776359 0.004677 80.66114 19.33886

10 0.004771 79.22177 20.77823


1 0.011012 5.161456 94.838542 0.012069 13.07879 86.921213 0.013397 24.89482 75.105184 0.015186 27.67688 72.323125 0.016288 29.31586 70.684146 0.017314 31.35384 68.646167 0.018422 32.66017 67.339838 0.019405 33.76115 66.238859 0.020332 34.75171 65.24829

10 0.021242 35.50073 64.49927


Variance decomposition can be defined as decomposition of variance due to changes in same series or other series in previous periods. Table 1 shows the variance decomposition of exchange rate with itself and with stock prices. The maximum decomposition of exchange rate with itself is 100% and with stock price is 25.29%. Table 2 shows the variance decomposition of stock price with itself and with exchange rates. The maximum decomposition of stock price with itself is 95.89% and with exchange rate

4-UNITED KINGDOM

TABLE 1.DESCRIPTIVE STATISTICS

UK EXCHANGE RATE UK STOCK RETURNS

Mean 1.000362 1.000449 Median 0.999591 1.001241 Maximum 1.027005 1.010131 Minimum 0.97158 0.984747 Std. Dev. 0.011039 0.004359 Skewness 0.19625 -1.23773 Kurtosis 2.950337 5.804355 Jarque-Bera 0.450003 40.22774 Probability 0.798515 0 Sum 69.02497 69.031 Sum Sq. Dev. 0.008286 0.001292 Observations 69 69

Descriptive Statistics is used to analyze the behavior of the stock returns and exchange rate . Descriptive statistics employed on the returns and exchange rate showed that UK has an average exchange rate of 100.03% percent with standard deviation of .011 percent and average return of 100.04 % with standard deviation of .004 %. The standard deviation of exchange rate during selected period is greater than stock price. So exchange rate shows higher level of risk than stock price.

TABLE 2 :CORRELATION

UK EXCHANGE RATE UK STOCK RETURNSUK EXCHANGE RATE 1 -0.16974UK STOCK PRICE -0.16974 1

Table 2 presents the correlation results for the UK .It is found that there exists no significant correlation among the stock prices and exchange rate .. Stock price has very weak negative correlation with the exchange rate. From the results of Correlation it is clear that there exist no positive correlation among the returns of stock market and exchange rate of UK. . Correlation Analysis is considered a weak technique to explore the integration among the markets because it only discusses the relationship and does not considers the lead lag relationship. So, Co-integration and Granger Causality are used to further investigate this issue.

TABLE 3:VARVAR

Sample: 1 70Included observations: 58

Lag LogL LR FPE AIC SC HQ

0 163.3558 NA 1.31E-05 -5.563994 -5.492944 -5.5363191 311.7379 281.4143 9.05E-08 -10.54269 -10.32954* -10.45966*2 312.4446 1.291469 1.01E-07 -10.42912 -10.07387 -10.290753 315.2627 4.956091 1.06E-07 -10.38837 -9.891022 -10.194644 320.8435 9.429594 1.00E-07 -10.44288 -9.803432 -10.19385 325.0374 6.796924 1.00E-07 -10.44956 -9.668017 -10.145146 329.394 6.7603 9.96E-08 -10.46186 -9.538216 -10.10208

7 339.7478 15.35215* 8.07e-08* -10.68096* -9.615212 -10.26583

8 340.6599 1.289532 9.08E-08 -10.57448 -9.366633 -10.1049 343.9657 4.44574 9.44E-08 -10.55054 -9.200596 -10.02471

10 345.955 2.538046 1.03E-07 -10.48121 -8.989161 -9.90002511 352.9518 8.444445 9.51E-08 -10.58455 -8.950401 -9.94801312 358.2242 5.999619 9.37E-08 -10.62842 -8.852177 -9.936538



TABLE 4: UNIT ROOT TEST

ADF LEVEL ADF FIRST DIFF PP LEVEL PP FIRST DIFFUK EXCHANGE RATEUK STOCK PRICES

-1.812447-0.09633

-6.049203-5.07134

-1.824824-0.073178

-8.120763-7.675494

CRITICAL VALUES1%5%10%

-3.53003-2.904848-2.589907

-3.531592-2.905519-2.590262

-3.528515-2.904198-2.589562

-3.53003-2.904848-2.589907

To run co-integration test it is necessary for the data to be stationary of same order. Above tests ensure that this data is non-stationary at level but becomes stationary at first difference. Data stationarity is tested through Augmented Dicky Fuller and Phillip Perron Tests as the later is not tha much strict in nature as is ADF test and both tests confirmed similar results (Dickey &

Fuller ,1981).Data is stationery of same order so we can test co-integration among stock price and exchange rate.

TABLE 5;CO INTEGRATION


HypoThesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None 0.13999 10.28965 15.49471 0.2591At most 1 0.000507 0.034463 3.841466 0.8527Trace test indicates no cointegration at the 0.05 level*denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values


Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None 0.13999 10.25519 14.2646 0.1957At most 1 0.000507 0.034463 3.841466 0.8527 Max-eigenvalue test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Trace test and Max-Eigen value test both represent the same results that Cointegration exists. Co-integration examines the co-movement among the time series. It does not identify the lead lag relationship. Granger Causality is used to determine the lead lag relationship.

TABLE 6:GRANGER CAUSALITY TESTS

Pairwise Granger Causality TestsDate: 06/14/12 Time: 08:45Sample: 1 69

Lags: 1


SER02 does not Granger Cause SER01 68 0.00747 0.9314 SER01 does not Granger Cause SER02 0.24828 0.62


TABLE:7 IMPULSE RESPONSE FUNCTION




.0000

.0025

.0050

.0075

.0100

.0125

1 2 3 4 5 6 7 8 9 10


.0000

.0025

.0050

.0075

.0100

.0125

1 2 3 4 5 6 7 8 9 10


-.001

.000

.001

.002

.003

.004

.005

1 2 3 4 5 6 7 8 9 10


-.001

.000

.001

.002

.003

.004

.005

1 2 3 4 5 6 7 8 9 10



1 0.012138 100 02 0.012495 98.42764 1.5723553 0.013521 87.05576 12.944244 0.01484 85.628 14.3725 0.015449 83.3071 16.69296 0.016261 80.6252 19.37487 0.017059 79.43437 20.565638 0.017716 77.9175 22.08259 0.018416 76.64257 23.35743

10 0.019078 75.69934 24.30066


1 0.004684 0.643229 99.356772 0.004856 4.267697 95.73233 0.004948 4.711501 95.28854 0.005198 11.6613 88.33875 0.005332 14.58809 85.411916 0.005468 16.2981 83.70197 0.005631 19.12583 80.874178 0.005763 21.04603 78.953979 0.005899 22.78463 77.21537

10 0.006036 24.592 75.408

Cholesky Ordering: SER01 SER02

Variance decomposition can be defined as decomposition of variance due to changes in same series or other series in previous periods. Table 1 shows the variance decomposition of exchangerate with itself and with stock prices. The maximum decomposition of exchange rate with itself is 100% and with stock price is 24%. Table 2 shows the variance decomposition of stock price with itself and with exchange rates. The maximum decomposition of stock price with itself is 99.35% and with exchange rate is 24%.

Conclusion:This paper examined the relationship between Stock market prices and Exchange Rate in European Countries for the period January 2002 to October 2007. We employed monthly data and apply Co integration and Granger Causality test to examine the relationship between stock prices and Exchange Rate. Our results

show that Co integration exists between exchange rate and stock returns. This study is beneficial for the investors who want to make investments in European market and it will help the investors to diversify their portfolio risk.

REFERENCES :

1.Mr.Lutfar Rehman (2009) Dynamic Relationship between Stock Prices and Exchange Rates .Evidence from Three South Asian Countries. International business research.

2.A.Iqbal ,N.Khalid(2011) .Dynamic relationship among the stock markets of India ,Pakistan and UK . World Academy of Science, Engineering and Technology 73 2011

3.Nadeem Sohail and Zakir hussain(2009)Long run and short run relationship between macro economic variables and stock prices in Pakistan.Pakistan economic and social reviewAggarwal, R. (1981),

4.Aggarwal(1981) Exchange rates and stock prices: A study of the US capital markets under floating exchange rates. Akron Business and Economic Review, Volume 12, pp. 7-12.

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Relationship Between Stock Price and Exchange Rate in European Countrie1 234