The document examines the relationship between stock prices and exchange rates in Germany and Sweden using daily data from 2011 to 2018. It finds: 1) There is a long-run cointegrating relationship between stock prices and exchange rates in Sweden. 2) There is a weak long-run cointegrating relationship between stock prices and exchange rates in Germany. 3) There is bidirectional Granger causality between stock prices and exchange rates in Sweden, but only unidirectional causality from exchange rates to stock prices in Germany. 4) There was no contagion effect from the German stock market crash in 2016 to the Swedish stock market. Volatility increased significantly during the 2016 crash.

1. The Analysis of Relationship between Stock Prices and
Exchange Rates: Evidence from Germany and Sweden
By
KEHINDE OGUNJEMILUSI

2. 2
Abstract
This report examines the interlinkages between a major stock market and another minor stock
market in Europe. It investigates the relationship between stock markets and exchange rates in
Germany and Sweden. The sample data consists of daily data over an 8 year period from January
2011 to December 2018 allowing the study of stock markets before and after the outcome of the
Brexit vote.
We employed the Engle-Granger Cointegration test, Vector Error Correction Model, Granger
causality test, Contagion Analysis as well as Volatility Modelling (GARCH) to analyse the
relationship between the two financial variables. Our findings show that there is an existence of
a long-run cointegrating relationship between the stock prices and exchange rates of Sweden and
also a weak cointegrating relationship between the stock prices and exchange rates in Germany.
Our findings also show a bidirectional causality relationship among the stock prices and the
exchange rates for Sweden. However, we observed only a unidirectional causal relationship from
the exchange rate to stock prices in Germany. Thus implying, there is no short-run association
from the stock prices to exchange rate in Germany. In terms of contagion, our estimated results
show no spill over effect from Germany stock exchange to Sweden stock exchange. We also
observed an intense volatility during the stock market crash that occurred in 24th June 2016.

3. 3
Table of Contents
Abstract .......................................................................................................................................2
LIST OF TABLES......................................................................................................................4
1.0 INTRODUCTION:...............................................................................................................5
2.0 LITERATURE REVIEW....................................................................................................5
3.0 DATA AND METHODOLOGY .........................................................................................7
3.1 DATA .....................................................................................................................................7
3.2 METHODOLOGY ...............................................................................................................7
4.0 DISSCUSSION OF FINDINGS...........................................................................................9
4.1 UNIT ROOT TEST ..............................................................................................................9
4.2 COINTEGRATION TEST ................................................................................................10
4.3 GRANGER CAUSALITY MODELLING (VECM).......................................................11
4.4 CONTAGION ANALYSIS ................................................................................................12
4.5 GARCH VOLATILITY RESULT....................................................................................13
5.0 CONCLUSION ...................................................................................................................14

4. 4
LIST OF TABLES
Table 1 Unit Root Test…………………………………………………………………………9
Table 2 Lag length selection Criteria…………………………………………………………..10
Table 3 Cointegration Result…………………………………………………………………..11
Table 4 VECM-Granger Causality…………………………………………………………….12
Table 5 Chow Test……………………………………………………………………………..12
Table 6 Contagion Analysis…………………………………………………………………….13
Table 7 Garch Output…………………………………………………………………………..13

5. 5
1.0 INTRODUCTION:
The aim of this study is to examine the relationship between stock markets and exchange rates
in Germany and Sweden. Stock prices can be significantly affected by numerous factors out of
which exchange rate fluctuations are a vital one. Since stock prices are majorly determined by
the present values of their future cash flows, it is expected that the values of their relative
currency will play a significant role in their price movements particularly for financial assets
held internationally. Thus, stock prices may influence or be affected by exchange rate dynamics.
Over the years, several economists have tried to forecast stock prices and returns in the area of
finance. The relationship between stock returns and exchange rates has drawn many attention of
economists for theoretical reasons due to the fact that they both play vital roles in predicting the
future trends for each other by investors and influencing the development of a country’s
economy.
The purpose of the study is to further investigate the relationship between stock prices and
exchange rate in Germany and Sweden.
The rest of the paper is organized as follows. The next section gives a brief review of empirical
studies, Section 3 explains the data and methodology employed. Section 4 presents the discussion
of results while Section 5 gives concluding remarks.
2.0 LITERATURE REVIEW
This study seeks to examine the interlinkages between stock prices and exchange rates. In other
words, changes in stock prices cause changes in exchange rates or vice versa. There have been
lots of empirical studies on the relationship between stock prices and exchange rates variables.
Abdalla and Murinde (1997) identified a unidirectional causality from exchange rates to stock
prices in three countries out of four developing countries. The authors' analysis identified that
stock prices Granger-cause exchange rates in the Philippines while exchange rates Granger-cause
stock prices in India, Korea and Pakistan.
Ajay, Friedman, and Mehdian (1998) employing the Granger Causality test, examining the
causal relations between stock returns and changes in the exchange rate. They found a uni-
directional causal relationship from the stock return to changes in exchange rates in all the
developed markets. In addition, they observed that a causal relationship flows from the stock
market to currency market in the Philippines but there are no causal relations are observed in the
emerging markets.

6. 6
Philaktis and Ravazzolo (2000) applying the cointegration technique and multivariate Granger
causality tests, investigated the long-run and short-run relationships between stock prices and
exchange rates in Philippines, Malaysia, Hong Kong, Indonesia, Singapore and Thailand. Their
findings revealed that no long-run relationship exists between the real exchange rate and the
stock market in each Pacific Basin country. The multivariate results show that the real exchange
rate and US stock prices are positively related to domestic stock prices for all the countries
Nieh and Lee (2001) examined the dynamic relationships between the stock prices and the
exchange rates for each G-7 countries (Canada, France, Italy, Japan, UK and the US) for the
period October 1, 1993 - February 15, 1996, using daily closing stock market indices and foreign
exchange rates. Their study, using Engle-Granger (EG) two-step and the Johansen maximum
likelihood cointegration test highlighted that there exist no long-run relationship between stock
prices and exchange rates in the G-7 countries.
Ali Kemal and Haider (2005) examined the short-run relationship as regards the movements of
exchange rate with the changes in prices, interest rates and trade balances in Pakistan. Their
results highlighted that there is no substantial correlation relationship between relative prices and
nominal exchange rate. However, changes in the real exchange rate and nominal exchange rates
are highly correlated.
Rahman and Uddin (2009) examined the relationship between stock prices and exchange rates
in three emerging countries; Bangladesh, India and Pakistan using Johansen cointegrating Test
and Granger Causality Technique. Their analysis found no cointegrating relationship between
stock prices and exchange rates. They also highlighted that there exists no causal relationship
between stock prices and exchange rates in the countries.
The purpose of this paper is to figure out the relationship between the two financial variables in
Germany and Sweden. The next chapter discusses the methodology, applies it to the data and
presents the empirical results. Section 5 concludes this study.

7. 7
3.0 DATA AND METHODOLOGY
3.1 DATA
The data for this study is based on stock market indices for Germany (DAX) and Sweden (Swedish
Krona) and Exchange rate – SEK/EUR and EUR/SEK with a daily frequency. The daily data were
employed in this study as against monthly data because it allows for deeper analysis of market-
based transmission in volatility. The sample covers the period 1st January 2011 to 3rd December
2018, allowing the study of stock markets and exchange rate before and after the outcome of the
Brexit vote. The data for stock prices and exchange rates were obtained from DataStream, with
the exception of the exchange rate- EUR/SEK. The data on the exchange rate of euro against the
Swedish krona (EUR/SEK) was extracted from the University of British Columbia pacific
exchange rate website (http://fx.sauder.ubc.ca/data.html). The data series have been transformed
into logarithmic form.
3.2 METHODOLOGY
The methodology that will be employed includes testing for unit roots using Augmented Dickey-
Fuller (ADF) test, testing for cointegration, using the Engle-Granger Cointegration Test and
Granger multivariate causality tests, testing for contagion using Forbes & Rigobon Test and also
testing for Volatility using GARCH to investigate the relationship between the two financial
variables.
In order to check the relationship between stock price and exchange rate, firstly it is essential to
determine whether the data collected is stationary or not. As a preliminary step, the Augmented
Dickey-Fuller (ADF) test is applied to check the unit-roots/stationarity of the data at level and first
differences if necessary. The ADF test is based on the following equation:
If the series are non-stationary, then we proceed to difference the data in order to induce
stationarity. In order to achieve this, we first estimate the Vector Autoregression model using the
level data in order to determine the lag length to be employed. The Schwarz Criteria is used to
identify the appropriate number of lags as they are considered the best criteria when the research
sample has more than 120 observations. The Schwarz Criteria considers both the closeness of fit
of the points to the model and also because it is traditionally used for financial data. If the series
is confirmed to be stationary, we proceed to carry out the cointegration test using the Engle-

8. 8
Granger cointegration test to examine the long-run relationship between the variables. A bivariate
model is carefully chosen in terms of analyzing the relationship between stock prices and exchange
rates, as illustrated below
EXt = β0 + β1SP + ε0
Where; EX= Exchange rate, SP= Stock prices and ε0= error term
After carrying out the cointegration test, if we observe that there exist a cointegrating relationship,
we carry out a Vector Error correction model (VECM). The ECM allows us to examine short-run
behaviour between the various stock markets. In addition, we implement the Granger causality
test looking at the bivariate relationship between stock prices and exchange rates in both countries.
i.e. if variable X granger causes Y and vice versa using the below equation;
1. EXt = α0+ β1Ext-1 + β2SP+ β2SPT-1 + ε0
2. SPt = α0+ β1SPt-1 + β2EX+ β2EXT-1 + ε0
The coefficient β2 is the coefficient measuring the influence of Xt−1 on Yt. If β2 =0, this means
that the previous values of X have no effect on Y and X does not granger causes Y. If β2 is
statistically significant and negative, we will conclude that X Granger causes Y. Therefore, we
examine if the relationship from Exchange rates to Stock prices as well as stock prices to
Exchange rates
The next step of analysis is the test for contagion and Volatility. The Forbes & Rigobon test is
employed to investigates the existence of contagion that relies upon a structural model of
interdependence. We seek to ascertain if there is a financial spillover effect from the Germany
Stock market crash that occurred in June 2016 on the stock market of Sweden1.
The null hypothesis is that there is no evidence of contagion in the series against the alternate
hypothesis that there is an evidence of contagion in the series.
H0: ƴ3= 0, Ha: ƴ3≠0
To carry out volatility testing, we employ a GARCH model using returns where we would further
confirm the Garch output does not suffer from zero correlation and heteroscedasticity. If the P-
1 https://en.wikipedia.org/wiki/2015%E2%80%9316_stock_market_selloff

9. 9
value is insignificant, then we assume there is no problem of zero correlation and the Garch
Output is correct
The results of our empirical analysis are presented in the next section.
4.0 DISCUSSION OF FINDINGS
4.1 UNIT ROOT TEST
As stated in the methodology, the first step is to ascertain the unit root tests on our variables. The
unit root tests reveal whether the variables are integrated of order zero, I(0), or they are integrated
of order 1, I(1). This was achieved by using Augmented Dickey-Fuller (ADF) test. Based on the
estimated result of the ADF and Graph, we fail to reject the null hypothesis of the existence of
unit root in levels for all the variables (see annexure 1). Hence, we proceed to difference the data
to achieve stationarity as required in time series data. Table 1 below shows the ADF result (first
difference). The P-value is statistically significant at 1% significance level, hence, we reject the
null hypothesis that the series are non-stationary and conclude that the series is stationary. The
graph of both variables as shown in Figure 1.0 above reveals that there is a mean-reverting
behaviour in the series. Thus, we conclude that all the series are integrated into the order (1)
processes.
TABLE 1- UNIT ROOTS RESULTS IN LEVELS AND FIRST DIFFERENCE
LEVELS FIRST DIFFERENCE
Countries Variables T-statistics P-value T-statistics P-value
Sweden SWEDEX -1.28664 0.6379 -57.18835 0.0001***
SWESP -1.491288 0.5381 -55.10416 0.0001***
Germany GEREX -1.241925 0.6582 -57.0307 0.0001***
GERSP -1.311351 0.6264 -54.8256 0.0001***
The asterisks *, **, *** indicates rejection of the null hypothesis of non-stationary at 10%, 5%,
and 1% levels respectively.

10. 10
Figure 1.0:
-.08
-.06
-.04
-.02
.00
.02
.04
.06
2011 2012 2013 2014 2015 2016 2017 2018
DAXRT
-.10
-.08
-.06
-.04
-.02
.00
.02
.04
.06
.08
2011 2012 2013 2014 2015 2016 2017 2018
OMXRT
Note: The stock markets and exchange rates are assumed to follow a Vector Autoregressive
Process (VAR(k)) which denotes the order of the autoregressive process. The Schwarz criterion
was chosen to select the optimal number of lags because it is traditionally used for financial data
and considers the closeness of fit of the points to the model. The result indicates that (1) lag length
should be used for all the variables.
Table 2:
Countries Variables No of lags SC value
Sweden
SWEDEX 1 -5.991103*
SWESP 1 -6.119895*
Germany
GEREX 1 -8.174672*
GERSP 1 -5.991677*
4.2 COINTEGRATION TEST
Since the series are stationary and integrated to the order of one (1), we proceed to analyse the
relationship between the variables with the econometric model-cointegration and causality using
the Engle-Granger Cointegration Test and/or Granger Causality Test. Cointegration indicates the
existence of a long-run equilibrium between the variables. If the residuals are stationary, then we
assume that the variables are cointegrated. That is, the variables move together in the long run.
Having established that each of the stock price indices is I (1), we implement the Engle-Granger
cointegration test looking at the bivariate relationship. If there exists cointegration between
variables, we proceed to use the residuals from the equilibrium regression to estimate the Vector
Error Correction Model (VECM) which measures the correction from disequilibrium. This
examines the long-run and short-run dynamics of the variables as well as the speed of adjustment.

11. 11
For each time series, the null hypothesis is that there is no long-run relationship and the alternate
hypothesis is that there is a co-integration relationship.
The cointegration output result for Sweden as indicated in Table 3 below indicates that the P-value
is statistically significant at a 1% significance level. Therefore, the null hypothesis that the
variables are not cointegrated is rejected at 1% significance level and we conclude that there exists
a cointegration between the stock market indices of Sweden and its exchange rate. However, we
noted a weak cointegrating relationship in Germany- DAX and EUR/SEK as the P-value is
statistically significant at 10% significance level.
Table 3: COINTEGRATION RESULT
Countries Direction Variables T-statistics P-value
Sweden EX|SP EX -7.555206 0***
Germany EX|SP GERSP -2.644843 0.0842*
The asterisks *, **, *** indicates rejection of the null hypothesis of non-stationary at the 10%,
5%, and 1% levels respectively
4.3 GRANGER CAUSALITY MODELLING (VECM)
Causality implies the existence of a short-run relationship between the variables. We implement
the Granger causality test looking at the bivariate relationship. The Granger causality test is based
on VECM due to the co-integrating relationship between the variables. With the Schwarz value,
the optimum orders of lags have been determined and used for VECM based Granger causality
models to allow each equation capture enough short-run dynamic and speed of adjusting using the
equation; ΔY= α+ β1Δyt-1+λ1Δxt+ λ1Δxt-1+ Πεt-1+ut
Where; Π= Vector Error Correction Model and Πεt-1- is the coefficient of the lagged residual terms
of the long-run relationship. It measures the speed at which Y returns to equilibrium after a change
in X. It must be negative and significant to conclude there is a causal relationship.
Tables 4 indicate the results of the Granger causality test. Based on the results, Sweden has a
bidirectional causality between its stock price and exchange rate in both the short-run and long-
run. That is, the direction of causality runs from the stock market to exchange (OMX to SEK/EUR)
as well as from exchange rate to the stock market (SEK/EUR to OMX). The result above indicates
that the p-value is statistically significant at 1% significance level, therefore we reject the null
hypothesis that stock price does not Granger cause exchange rate in Sweden and conclude that the

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stock return in Sweden stock market Granger causes the exchange rate in Sweden and vice versa;
there is an evidence of a causal relationship (short-run dynamics) between the two variables.
Furthermore, we observed a unidirectional causal relationship in Germany which runs from the
exchange rate to the stock price in both short-run and long-run. There is no interaction from stock
price to exchange (i.e. DAX to EUR/SEK). The result above indicates that the p-value is not
statistically significant at any significance level, therefore we fail to reject the null hypothesis that
the DAX does not Granger cause EUR/SEK and conclude that the stock return in Germany stock
market does not granger causes the exchange rate in Germany; there is no evidence of a causal
relationship (short-run dynamics) that flows from DAX to EUR/SEK.
Table 4: Granger Causality-VECM
Country Granger cause No of lags Residual (Coefficient) P-value
Sweden
EX-SP 1 -0.012823 0.0067
SP-EX 1 -0.056381 0
Germany
EX-SP 1 -0.007785 0.0017
SP-EX 1 -0.001305 0.4428
4.4 CONTAGION ANALYSIS
Based on the graph plotted and news obtained, we ascertain that a stock market crash occurred on
24th June 2016 due to Brexit vote. Therefore, since the breakpoint date is known, a chow test was
carried out in order to further confirm if the structural break occurred in June 2016. The chow test,
test the null hypothesis of no breaks at specified breakpoints against the alternate hypothesis that
there are breaks at specified breakpoints. Table 6 shows the result of the chow test. The result
indicates that the P-value of the F-statistics is statistically significant. Hence, we reject the null
hypothesis of no breakpoints in the series and conclude that there exists a structural break in the
time series data.
Table 5: CHOW TEST
Chow test
F-statistics P-value
653.9021 0***
In order to deal with the problem of structural breaks to avoid the overall result from being
spurious. We divide the time period has been divided into two parts. (The pre-crisis period and the
post-crisis period). The pre-stock market crash began from 1st January 2011 to 23rdJune 2016. The

13. 13
stock market crash commenced on 24th June 2016 after which the stock markets became stable on
29th June 2016
Having established the stationarity and structural break dates in the series a contagion analysis test
were carried out using the Forbes & Rigobon Test. This test seeks to ascertain if there is a financial
spill over effect from Germany Stock market crash that occurred on 24th June 2016 on the stock
market of Sweden.
Table 7 shows the contagion output. The P-value is statistically significant at 1% significance
level; therefore, we reject the null hypothesis of no contagion and conclude that there is evidence
of contagion in the series. This implies that the behaviour of the Germany stock market has a
significant positive effect on Sweden stock market; any increase in Germany. stock market returns
results in an increase in Sweden stock market returns. Thus, there is an existence of volatility
spillover effect between the stock returns in Germany stock market and Sweden stock market.
Table 6: CONTAGION OUTPUT
Variable Coefficient Std. Error t-Statistic Prob.
C 0.013099 0.012595 1.040048 0.2984
DUMMY -0.015763 0.023176 -0.68014 0.4965
OMXSCALED 0.796651 0.01228 64.87544 0
DUMMY*DAXSCALED 0.36576 0.03372 10.84685 0
4.5 GARCH VOLATILITY RESULT
Using the GARCH model and taking into consideration the ARCH and GARCH effect, we
ascertain if there is high volatility within the two countries stock market indices- DAX and OMX.
The ARCH effect signifies the presence of autocorrelation and heteroskedasticity issues in data.
It is observed by defining the mean equation and checking the residuals for Serial-Correlation and
heteroscedasticity. When we highlight an ARCH effect in the model (No serial correlation and
heteroscedasticity), we then examine the volatility existence between the Germany stock market
and Sweden stock market.
Table 8 indicates the GARCH output result. Based on the above result, all the estimates parameters
of the GARCH output are statistically significant at a 1% significance level. This indicates that in
the pre-stock market crash time period, the conditional variances of the series are affected by their
own past shocks and past volatility. Also, the constant term is quite low indicating that the long-

14. 14
term average weighted value of conditional variance has a small effect on today‘s conditional
variance.
Table 7: GARCH OUTPUT
Coefficient Std. Error z-Statistic Prob.
C 1.42E-06 2.82E-07 5.026993 0
RESID(-1)^2 0.093561 0.008478 11.03562 0
GARCH(-1) 0.869698 0.013864 62.72848 0
The α measures the degree to which shocks to today’s returns feed through into volatility of the
next period, thus the Arch coefficient (0.09) indicates a relatively stable short-term volatility while
the long-term effects of past shocks on returns measured by the GARCH parameter ß (0.87) are
within the normal range.
The sum of the ARCH and GARCH effects (α+β) measures the rate in which this effect dies over
time (volatility persistence), the result (0.96) indicates this is less than one, implying that the
effects of shocks decline very slowly.
In addition, the conditional variance graph below indicates there was intense spiked volatility
occurring in the 2nd quarter of 2016.
.0000
.0001
.0002
.0003
.0004
.0005
.0006
.0007
2011 2012 2013 2014 2015 2016 2017 2018
Conditional variance
5.0 CONCLUSION
This study investigates the relationship between stock price and exchange rate in Germany and
Sweden. The results of the study show that there is a long-run cointegration relationship among
the variables. In addition, we observed that stock prices Granger cause exchange rates and vice
versa in Sweden while a unidirectional causality occurs in Germany from exchange rate to stock

15. 15
prices but not vice versa. The results of this study also indicate that any movement in the foreign
exchange markets can substantially increase fluctuation in Germany and Sweden. Therefore,
authorities and investors try to respond to foreign-exchange market adjustment and react to its
positive and negative shocks.
Furthermore, this study highlights a financial spillover effect from Germany Stock market crash
that occurred on 24th June 2016 on the stock market of Sweden, thus the presence of a contagion.
The Garch output and the conditional variance also reveal there was intense spike volatility
occurring in the 2nd quarter of 2016.
The implication of these findings is that since the stock market and currency markets for both
countries- Germany and Sweden are correlated on the long run and short run, long term investment
to investors might not be worthwhile. Therefore, it is important that fund managers take
precautionary measures when diversifying portfolios due to interdependency.
.

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Granger, C.W., Huang, B., Yang, C. (2000), A bivariate causality between stock prices and
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