Transcript Volatility Spillovers between Stock Returns and Foreign Exchange

By
Qurat-ul-ann Azmat
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Introduction
 Relationship between Stock Market and Foreign Exchange
Market.
 Advent of floating exchange rate in 1973, reforms of
financial markets in early 1990s and Asian currency crisis in
1997-1998 have established a strong pitch for the dynamic
relation ship between stock and foreign exchange markets
(Mishra, 2004).
 Floating exchange rate appreciation reduces the
competitiveness of export markets; and has a negative
effect on the domestic stock market (Yucel and Kurt, 2003).
 For import dominated country, it may have positive effect
on the stock market by lowering input costs (Adjasi et.al;
2008).
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Empirical Evidence
 Some studies show significant positive relationship
between the two markets (Cheung and Westermann ;
2000).
 While others show significant negative correlation
between stock market and local currency (Solnik;
2000).
 There are studies which have found weak or no
relationship between the two markets (Bodart &
Reding ;1999).
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 Seven South Asian countries- Bangladesh, Bhutan, India,
Maldives, Nepal, Pakistan and Sri Lanka- formed the South
Asian Association for Regional Cooperation (SAARC) in
1985.
 They formed the SAARC Preferential Trading Agreement
(SAPTA) in 1993 and transformed it into South Asian Free
Trade Area (SAFTA) in 2004 with a view to enhancing their
productive capacity and the region’s trading interests.
 Under the World Bank designated category, amongst the
seven countries India, Pakistan Sri Lanka are considered
developing countries.
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Aim of Study
 To investigate the dynamic relationship between
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foreign exchange market and stock market among the
three SAARC countries i.e India, Pakistan and
Srilanka.
If there is some link between these two markets how
volatility in one market spill over to other market.
Time period (2001-2009)
Long run relationship :Engle Granger approch.
Volatility spillover: EGARCH
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Implication
Improved knowledge of volatility spillover effect
between the stock and currency markets, and
consequently the degree of their integration, will
expand the information set available to international
portfolio managers, multinational corporations, and
policymakers for decision-making.
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Literature Review
 Flow oriented models [Dornbusch and Fisher
(1980)]
This model posits that currency movements affect
international competitiveness and balance of trade
positions and consequently, the real output of the
country, which in turn, affects the current and future
expected cash flows of firms and their stock prices.
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 Stock oriented models [Branson and Frankel
(1983)]
 Agents should allocate their entire wealth among
domestic and foreign assets including currencies in
their portfolio.
 An increase in domestic stock prices leads individuals
to demand more domestic assets. To buy more
domestic assets, they are required to sell foreign assets
as they are relatively less attractive now. As a result of
which there is an appreciation of local currency due to
more demand of domestic assets.
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 Kanas (2000) first uses EGARCH models in investigating
the volatility spillover effects in US, Canada, Japan, UK,
France, and Germany for the period between 1986 and
1998, his study shows significant symmetric spillover
effects from stock market returns to foreign exchange rate
changes.
 Yang and Doong (2004) invested the same phenomenon
using G-7 countries. The results point to significant
volatility spillovers and an asymmetric effect from the stock
market to the foreign exchange market for France, Italy,
Japan and the US, suggesting integration between stock
and foreign exchange markets in these countries.
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 Wu (2005) investigated the same phenomenon among
Japan, South Korea, Indonesia, Philippines, Singapore,
Thailand and Taiwan for the period 1997-2000
splitting the sample into crises and recovery periods.
He found a bi-directional relationship between the
volatility of stock returns and exchange rate changes
during the recovery period in all countries except
South Korea, as well as significant contemporaneous
relationships between the two markets for most of the
countries. Furthermore, he found volatility spillovers
increased in the recovery period.
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Data
 Monthly average data related to stock indices and spot
exchange rates was used.
 For Pakistan KSE-100 index, India S&P CNX Nifty and
Srilanka Colombo all share index prices were used.
 Spot exchange rates of relevant currency of each
country was taken in terms of US dollar.
 Data sources: Yahoo finance and oanda.com.
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Methodology
 Following Kanas (2000) continuously compounded stock returns and
exchange rate changes were calculated as the first differences of the natural
log.
Stock returns=Lnp(t)-Lnp(t-1)
Exchange rates= Lnp(t)-Lnp(t-1)
 Following Qyyum and Kamal (2006) for testing the existence of co
integrating relationship between the stock market prices and the
exchange rate Engle and Granger (1987) two step method was used.
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 For volatility analysis bivariate extension of the
EGARCH (p,q) model was applied in order to examine
whether the volatility of stock returns affects and is
affected by the volatility of exchange rate changes
within each economy.
 The EGARCH specification (Nelson, 1991) is used in
order to test whether the volatility spillover effects are
asymmetric.
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 Conditional mean equation
 Conditional Variance equation
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Empirical Results
 Descriptive Statistics
Stock Returns and Exchange rate changes
Mean
Std. Dev
Skewness
Kurtosis
JB
Pakistan
.01
.08
-1.05
6.27
India
.01
.06
-.82
4.91
Srilanka
.018
.06
-.17
3.09
67.59*
(.00)
28.35*
(.00)
.58
(.74)
Mean
Std. Dev
Skewness
Kurtosis
JB
Pakistan
.003
.01
2.7
12
India
.00004
.01
1.89
12.15
Srilanka
.002
.01
-.90
1.66
496.7*
(.00 )
437.17*
(.00)
8.16*
(.00)
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Results of Augmented Dickey Fuller Test
Stock Returns
Exchange rate changes
Pakistan
-3.784284*
-3.046597*
India
-4.175236*
-2.685836*
Srilanka
-3.890565*
-3.980353*
All of them are significant at 1%
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Cointegration Results: Long term relationship
and ADF test on residuals
Pakistan
India
Srilanka
20861.51
8402.035
1793.443
(17.58558)
(9.928690)
(1.238160)
160.4537
(32.58014)
37.25860
26.91875
(23.14931)
(6.626548)
-352.4540
-170.4810
-15.49011
(-17.07238)
(-9.547697)
(-0.957304)
R(square)
0.911068
0.885786
0.796102
ADF
-2.346227
-2.371289
-2.473763
(-3.4946)
(-3.4946)
(-3.4946)
Intercept
Trend
Exchange rates
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EGARCH Estimation
Estimated Parameters
Pakistan
India
Sri lanka
11.47445*
7.20215
0.7743
0.0357
0.2571
-0.23908
6.63594
-5.91753
0.9906
0.7052
1.367
-0.5985
-0.788908*
-0.558645
0.0029
0.2078
47.33409*
21.20923
0.0017
0.2387
-6.70373*
-5.42002*
0.0209
0.0315
-0.43489
-1.0322*
10.72319
Volatility Persistence (stock returns)
probability
Spillover: from Stock Returns to
Exchange Rates
probability
Asymmetric Spillover effect: From
Stock Returns to Exchange Rates
0.2
probability
6.532961
volatility persistence (exchange rates)
probability
Spillover: from Exchange Rates to
stock returns
probability
Asymmetric Spillover effect: from
Exchange Rates to stock returns
1.3932
-1.15801
0.6353
0.495769
0.0511
probability
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0.0932
0
Conclusion
 The estimated results from cointegration analysis show
that there is no long run relationship between the two
markets.
 Results show that volatility spillover effects from exchange
rate changes to stock returns is significant in case of India
and Sri Lanka which shows the integration of two markets
while in case of Pakistan there is no significant spillover in
both the markets.
 Finally, for the asymmetric spillover effects result show that
this effect is negative and significant for India from stock
returns to foreign exchange rates and from exchange rates
to stock returns in case of Sri Lanka.
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References
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