Linkages between extreme stock market and currency returns

Linkages between extreme stock market and
currency returns
Phornchanok Cumperayot a,*, Tjeert Keijzer b
, Roy Kouwenberg c
a
Chulalongkorn University, Faculty of Economics, Bangkok 10330, Thailand
b
AEGON Asset Management NL, AEGONplein 20, 2591 TV, The Hague, Netherlands
c
Asian Institute of Technology, School of Management, P.O. Box 4, Pathumthani 12120, Thailand
Abstract
We investigate the link between extreme events on the currency and stock markets for 26 countries by
estimating a simultaneous equations probit model, using a sample of 2500 daily returns in the period from
1996 to 2005. In a number of emerging markets that went through a period of crisis an extreme stock mar-
ket decline increased the probability of extreme currency depreciation on the same day. For currency mar-
kets we find evidence of spillover of extreme events within regions, but limited influence outside the
region. Extreme events on stock markets are much more interrelated globally, particularly when they orig-
inate from the US.
2006 Elsevier Ltd. All rights reserved.
JEL classification: F31; F37; G15; C35
Keywords: Currency market; Stock market; Extreme events; Spillover
1. Introduction
The Asian crisis of 1997 sent the region into a prolonged period of currency depreciations
and stock market declines, with grave consequences for the real economy during the subsequent
years. As the crisis unfolded initially in Thailand with the devaluation of the Baht in July,
* Corresponding author. Tel.: þ66 2 218 6241; fax: þ66 2 218 6201.
E-mail address: phornchanok.c@chula.ac.th (P. Cumperayot).
0261-5606/$ - see front matter 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jimonfin.2006.01.007
Journal of International Money and Finance 25 (2006) 528e550
www.elsevier.com/locate/econbase

policy makers in countries that were still unaffected by the crisis e such as Malaysia and
Korea e still had some time to react and could have taken measures to protect their currencies.
However, at that time a full-blown regional crisis was not anticipated. A relevant question is
whether policy makers in Asia could have used the rapidly increasing number of extreme events
in the region, and the increased volatility in their local stock markets, as early warning signals
of further extreme currency depreciations in the region. In turn, could investors have anticipated
the effects of what began as a currency crisis on global equity markets in general and Asian
equity markets in particular?
This study investigates the empirical link between extreme returns in local stock and cur-
rency markets and the pattern of spillover within and across regions.1
By focusing on extreme
events, we aim to isolate the effect of true market shocks from the ‘‘normal’’ pattern of returns,
in order to gain insight into the mechanics of possible cross-market spillover effects in extraor-
dinary market environments. The first question that we would like to answer is whether extreme
stock market returns and extreme currency market returns tend to occur simultaneously and
whether one market leads another. If the latter is the case, can the occurrence of an extreme
return in the stock (currency) market then be used to predict future extreme returns in the cur-
rency (stock) market? And apart from these effects in local markets, do we find evidence of
spillover of extreme returns from one region to another?
To address these questions we estimate a country-specific bivariate simultaneous equations
probit model, with the probabilities of extreme currency and stock market events as dependent
variables. We focus on extreme events across a large selection of countries (26) and a long sam-
ple of daily data (2500 days). A number of authors have applied bivariate or multivariate ex-
treme value theory (EVT) to study spillover behavior of extreme stock market returns, such
as Straetmans (2000), Longin and Solnik (2001) and Poon et al. (2004). Hartmann et al.
(2003a,b) apply EVT to extreme returns and spillover in currency markets, while Hartmann
et al. (2004) address the link between extreme returns in stock markets and bond markets.
We are not aware of any papers in the multivariate EVT literature that focus on the link between
stock market extremes and currency market extremes.
The approach to modelling extreme linkages applied in this paper is, however, not based
on EVT, but on limited dependent variable models, similar to Bae et al. (2003). Whereas Bae
et al. (2003) use the total number of extreme events in a region as the dependent variable in
order to study contagion effects in global stock markets, we try to predict the occurrence of
extreme events for 26 individual countries. Moreover, we investigate the contemporaneous
link between extreme events in stock markets and currency markets. Other papers related
to our research include Kaminsky and Reinhart (1999) and Glick and Hutchison (2001),
who study the problem of linkages between currency and banking crises. These papers focus
on low-frequency data (monthly and annually, respectively) and use macroeconomic variables
to explain and/or predict currency and banking crises. Glick and Hutchison (2001) model
the linkage between currency and banking crises with a simultaneous equations probit
model. Phylaktis and Ravazzolo (2005) study short- and long-run dynamic links between
stock prices and exchange rates in the Pacific Basin region in a cointegration relationship
framework using monthly data. Our paper complements the work of Phylaktis and Ravazzolo
1
Financial spillover occurs when extreme market returns in one specific market or country trigger a similar extreme
event in a different, yet adjacent, market or country. Due to the ambiguity surrounding the definition and measurement
of the related concept ‘‘contagion’’ (see, e.g. Dornbusch et al., 2000; Karolyi, 2003), we focus exclusively on spillover
effects of extreme returns.
529
P. Cumperayot et al. / Journal of International Money and Finance 25 (2006) 528e550

(2005) by focusing on extreme events, using daily data for 26 developed and emerging
markets.
The empirical results presented in this paper show that an extreme stock market decline
increased the probability of an extreme currency depreciation on the same day in a number
of emerging markets that went through a period of crisis. The opposite effect, from cur-
rency depreciation to stock market decline, is not supported strongly by the data. In a num-
ber of countries an extreme depreciation actually decreased the probability of a stock
market decline, probably reflecting the positive effect of currency depreciation on exports
and growth. For currency markets we find evidence of spillover of extreme events within
regions, but limited effects outside the region. Extreme events on stock markets are
much more interrelated globally, with events in the region America having the strongest im-
pact worldwide.
This paper proceeds as follows: Section 2 describes the research methodology and modelling
approach adapted in this paper, as well as our definition of extreme events. Section 2 also re-
views the theoretical literature motivating our empirical work. Section 3 provides a description
of the data, the procedure for generating out-of-sample conditional volatility predictions and an
analysis of conditional extreme event probabilities, providing a first impression of the charac-
teristics of the data. In Section 4, we present the estimation results of the simultaneous equa-
tions probit models. Section 5 summarizes results and concludes the paper.
2. Methodology
Since we aim to study market linkages in times of stress, we first need to specify our def-
inition of extreme returns. Similar to Bae et al. (2003), we select the a%-percentile e with a
located in the tail area (e.g. a ¼ 1%, 2.5%, or 5%) e of the return distribution as the threshold
for the definition of extremes. We focus on the left tail of the stock market return distribution,
i.e. the a% most negative stock market returns, and study the right tail of the currency returns,
i.e. the a% largest increases of the foreign currency price of 1 US dollar, representing depre-
ciation (or devaluation) of the local currency. Typically, periods of economic crisis involve
a weakening of the local currency and for this reason we ignore the left tail of the currency
return distribution, i.e. extreme appreciation or revaluation.
The binary variable yt,i
c
indicates the occurrence of an extreme currency return in country i on
day t:
yc
t;i ¼ 1; if the currency return (measured in local currency per US dollar) of country i
on day t is above the (1 a)%-percentile threshold,
yc
t;i ¼ 0; otherwise.
Similarly, the binary variable yt,i
s
indicates the occurrence of an extreme stock market return
in country i on day t:
ys
t;i ¼ 1; if the stock market return in local currency on day t in country i is below the a%-
percentile threshold,
ys
t;i ¼ 0; otherwise.
We model the probability of the occurrence of extreme stock and currency market events
with a simultaneous equations probit model for each country i. This model allows us to estimate
530 P. Cumperayot et al. / Journal of International Money and Finance 25 (2006) 528e550

to what extent extreme returns in the currency market and the stock market of a country are
linked to each other and to a number of additional explanatory variables.2
The simultaneous
equations probit model is defined as follows:
yc
t;i ¼ g1iys
t;i þ b0
1;iX1t;i þ 31t;i; for each country i and t ¼ 1;2;.;T ð1Þ
ys
t;i ¼ g2iyc
t;i þ b0
2;iX2t;i þ 32t;i; for each country i and t ¼ 1;2;.;T ð2Þ
with yc
t;i ¼ 1; if yc
t;i 0;
yc
t;i ¼ 0; otherwise;
and ys
t;i ¼ 1; if ys
t;i 0;
ys
t;i ¼ 0; otherwise:
The matrix X1t,i (X2t,i) contains observations of k1 (k2) exogeneous explanatory variables at
time t for predicting currency (stock) market crises in country i. The variables 31t,i and 32t,i are
the disturbance terms of Eqs. (1) and (2), which are assumed to be identically and indepen-
dently distributed (IID).
Note that Eqs. (1) and (2) are not estimated separately with two probit-regressions: this
would lead to biased and inconsistent parameter estimates due to the presence of endogenous
variables on the right-hand-side of the equations. An instrumental variable estimation technique
can be applied to circumvent this problem: the simultaneous equations probit model above can
be estimated with a two-stage estimation procedure, as described in Maddala (1983; pp.
246e247). The covariance matrix of the estimated coefficients is non-standard and is adjusted
accordingly in the estimation procedure (see Maddala, 1983, p. 247).
The probit model allows us to estimate the contemporaneous relationship between extreme
events in the currency market and the stock market. The literature provides diverse theories
concerning the precise nature of this relationship (see, e.g. Phylaktis and Ravazzolo, 2005).
We first concentrate on the link from exchange rate depreciation to the stock market. The cur-
rent account is one channel through which a strong exchange rate depreciation (or devaluation)
can affect the probability of a stock market decline. A currency depreciation increases the local
currency value of exports and export competitiveness, leading to increased economic growth
and corporate profits (see, e.g. Junz and Rhomberg, 1973). The stock market reflects future
profit growth (see Fama, 1990; Schwert, 1990) and a strong depreciation can therefore lead
to a lower probability of a simultaneous extreme stock market decline.3,4
A large unanticipated depreciation or devaluation can also have a negative impact on the
economy, if domestic banks and firms have large amounts of unhedged foreign currency de-
nominated debt. The increased debt burden leads to a decline in investment and economic ac-
tivity (Mishkin, 1996). A currency collapse can also undermine an already ailing banking
sector, leading to a combined currency and banking crisis (a twin crisis, see Kaminsky and
Reinhart, 1999). Not every strong currency depreciation in emerging markets leads to a banking
crisis or a reduction of future economic activity. Following an extreme currency market event,
2
See Glick and Hutchison (2001) for an application of the simultaneous equations probit model to twin banking and
currency crises.
3
As stock prices are the present value of all future dividends, we expect limited impact of the short-term J-Curve
effect (see, e.g. Magee, 1973).
4
An exception to this relationship might be trade-oriented countries where exports possess a significant import con-
tent, such as Singapore (Abeysinghe and Yeok, 1998).
531

however, foreign investors might not yet know (or disagree on) whether the depreciation will be
beneficial or detrimental to the economy in the longer term. The increased level of uncertainty
may lead to a higher country risk premium and as a result, lower equity prices.5
We now turn our attention to the link from extreme stock market declines to the currency
market. In countries with an open, relatively small and undeveloped domestic capital market,
portfolio flows of foreign investors can exert a strong influence on the local stock market (Be-
kaert et al., 2002). If foreign investors drastically lower their return expectations or increase
their risk assessment of the local stock market, this could lead to large portfolio outflows
and a stock market decline. In turn, portfolio outflows can lead to excess supply of the local
currency and depreciation of the exchange rate, as illustrated in the theoretical model of Hau
and Rey (2006). Froot and Ramadorai (2002) and Hau and Rey (2004) provide empirical evi-
dence of the positive relationship between portfolio flows and currency returns. Hence, in
emerging and small developed markets, we would expect extreme stock market declines to
be linked to an increased probability of extreme currency depreciation through foreign investor
portfolio flows. For large developed markets, however, where foreign investors are less domi-
nant in the capital markets, we do not anticipate the link between extreme stock market declines
and currency depreciation with the same conviction. This is due to the many economic factors
that can influence the relationship, such as differences in inflation rates, domestic growth versus
growth abroad, imports and exports.
3. Data, volatility and conditional probabilities of extreme events
3.1. Data
We use daily data on local stock index returns and exchange rates from the period 3 July
1995 through 29 July 2005, a sample of 2630 daily return observations.6
In total 26 countries
are included in our study, consisting of 17 emerging markets and nine developed markets. We
use SP/IFCI stock indices in local currency for the emerging markets, which are available on
a daily basis starting from 30 June 1995. FTSE World stock indices in local currency represent
the developed stock markets. The FTSE World index series are value-weighted total return in-
dices. Daily exchange rates for all countries versus the US dollar are from Datastream (original
source: WM Company/Reuters). We measure exchange rates relative to the US dollar, as the US
dollar is the main currency for invoicing international exports and imports.7
For the United
States, we use the FED Nominal Broad Dollar Index.
5
Ethier (1973) argues that a strong depreciation could lead to an increase of exchange rate risk and theoretically
lower exports due to the reaction of export firms to higher profit risk. On the other hand, De Grauwe (1988) shows
that an increase in exchange rate risk can also lead to an increase in output and trade, depending on the level of risk
aversion of firms. The existence of financial markets and hedging instruments is also an argument against exchange
rate volatility having a negative effect on trade.
6
There are three exceptions. Stock market returns for the Czech Republic are available from 1 January 1996 (2500
observations). Stock market data for Russia starts from 4 February 1997 (2214 observations), while the Russian ex-
change rate returns are available from 7 March 1996 (2452 observations).
7
For example, rice exports from Thailand to countries in Asia and Africa are typically not invoiced in Thai Baht, but
in US dollar. Even in a large developed country like the United Kingdom, which has a widely accepted currency, the US
dollar is used for invoicing 20% of exports to the neighbouring Eurozone (figures for 2001, source: HM Revenue
Customs). We could use trade-weighted exchange rates instead, however, the weights do not reflect the currencies
used for trade invoicing, but the aggregate flow of trade to different countries. Standardized statistics on currencies
used for invoicing of international trade are not available for the countries in our sample.

A drawback of using daily returns in comparison to lower frequency data, e.g. monthly, is
that we cannot obtain daily data of macroeconomic series such as GDP growth and inflation.
Unfortunately, the available monthly time-series of returns and macroeconomic variables are
relatively short, especially for emerging markets, and therefore the number of extreme returns
in the far end of the tails is small. We consequently opt for daily data, which gives us sufficient
observations to measure extreme values. Our modelling approach will focus on time-series
models, using lagged return data to predict the probability of extreme returns in currency
and stock markets.
Table 1 summarizes the basic descriptive statistics of both daily currency (Panel A) and
stock market returns (Panel B), for the period 3 July 1995 until 29 July 2005. All statistics
shown are based on log-returns, multiplied by 100. Countries are grouped per region and clas-
sified as either a developed or an emerging market. In Panel A, the high exchange rate volatility
of countries that went through a crisis stands out, e.g. Argentina, Brazil, Indonesia, Korea, Ma-
laysia, Thailand, Russia and Turkey. Panel A also illustrates that the exchange rate distribution
is positively skewed in most emerging markets due to frequent devaluations relative to the US
dollar, whereas a negatively skewed distribution is more common in developed markets. The
kurtosis of the returns series is very high for all emerging markets and the developed countries
in Asia, indicating that large outliers frequently occur. JarqueeBera tests furthermore reject
normality for all markets.8
The descriptive statistics in Panel B of Table 1 also reveal the influence of extremes on
the stock market return distribution, as indicated by the large kurtosis of most series. In all
cases JarqueeBera tests again reject normality. Many emerging countries have a positively
skewed stock return distribution, which is probably related to frequent devaluations of the
local currency and subsequent or simultaneous improvements in the local stock market. As
we want to separate the currency effects from the stock market returns in our analysis of
market linkages, in this paper we study local currency stock returns, and not US dollar
stock returns.
3.2. Expanding window estimation of conditional volatility
It is well known that daily exchange rate and stock market returns display volatility clus-
ters, which can be modelled using an ARCH- or GARCH-type model as originally proposed
by Engle (1982) and Bollerslev (1986). As we expect extreme returns to occur more fre-
quently in periods of increased volatility, we aim to use the out-of-sample forecast of con-
ditional volatility as an explanatory variable in the bivariate probit model for extreme
returns.
In order to generate out-of-sample volatility forecasts, we employ an expanding window ap-
proach that re-estimates a GARCH model on a weekly basis and then predicts the conditional
volatility 5 working days ahead. We initialize the procedure with 130 daily observations from 3
July 1995 through 29 December 1995 as the first estimation window. Market volatility for the
next 5 working days (1 January 1996 through 5 January 1996) can then be predicted with the
estimated model. The second estimation period is from 3 July 1995 through 5 January 1996.
Again, the estimated model is used to predict volatility for the next 5 working days (8 January
1996 through 12 January 1996). Gradually expanding the window of observations that way, we
8
Test results not displayed in Table 1 to save space. It is well documented that foreign exchange rate returns are
heavy-tailed distributed (see, e.g. Koedijk et al., 1990).
533

Table 1
Descriptive statistics of currency and stock market returns
Region Country Panel A e Exchange rate returns Panel B e Equity market returns
Mean Min. Max. Std. Dev. Skewness Kurtosis Mean Min. Max. Std. Dev. Skewness Kurtosis
America
Emerging
Argentina 0.0421 13.35 33.65 1.15 13.1 359.1 0.0607 14.71 15.18 2.11 0.0 9.5
Brazil 0.0360 11.78 10.80 0.98 0.5 31.4 0.0850 15.86 25.62 1.95 0.4 21.5
Chile 0.0129 2.63 3.58 0.50 0.3 7.2 0.0377 5.82 6.74 0.90 0.1 7.7
Mexico 0.0128 5.25 4.76 0.56 0.6 13.5 0.0664 11.44 11.51 1.45 0.1 8.9
Peru 0.0138 1.83 1.43 0.23 0.1 9.5 0.0489 9.56 6.33 1.20 0.3 9.5
America
Developed
Canada 0.0043 1.70 1.66 0.40 0.1 4.7 0.0443 8.15 5.27 1.05 0.6 8.4
United States 0.0063 1.51 1.06 0.26 0.0 4.6 0.0344 7.00 5.32 1.15 0.1 6.1
Asia Australia
Emerging
India 0.0085 2.21 3.08 0.24 0.9 31.3 0.0448 12.69 8.40 1.57 0.3 7.3
Indonesia 0.0582 20.42 23.94 2.00 1.5 42.5 0.0399 16.83 17.14 2.17 0.1 12.2
Korea 0.0112 19.79 13.65 1.03 0.7 105.9 0.0277 13.07 10.97 2.32 0.0 5.9
Malaysia 0.0156 13.38 35.69 1.02 15.3 637.8 0.0007 23.69 21.01 1.75 0.8 40.3
Philippines 0.0304 11.10 12.63 0.66 1.4 98.1 0.0131 10.59 15.31 1.57 0.7 14.6
Taiwan 0.0063 2.62 4.53 0.29 2.3 43.4 0.0150 10.44 8.67 1.75 0.0 5.2
Thailand 0.0201 6.24 7.06 0.71 0.4 31.0 0.0197 12.09 12.72 2.06 0.5 7.7
Asia Australia
Developed
Australia 0.0009 5.29 3.05 0.67 0.0 6.1 0.0435 6.67 5.51 0.78 0.4 8.1
Japan 0.0033 6.58 3.57 0.69 0.7 8.9 0.0061 6.43 6.94 1.24 0.0 5.2
New Zealand 0.0018 4.06 3.52 0.71 0.2 5.8 0.0251 15.69 10.78 1.06 1.1 28.6
Singapore 0.0064 3.96 2.48 0.38 0.9 17.8 0.0023 10.28 17.86 1.56 0.6 14.8
Europe
Emerging
Czech Republica
0.0029 3.58 8.26 0.69 0.5 12.0 0.0570 7.16 8.64 1.47 0.1 5.1
Hungary 0.0156 3.48 4.73 0.61 0.1 6.4 0.1088 13.96 11.62 1.85 0.4 9.7
Poland 0.0122 3.17 4.28 0.60 0.5 7.1 0.0530 10.33 7.27 1.65 0.1 5.3
Russiab
0.0726 35.81 48.25 2.12 4.9 225.0 0.1270 23.76 28.23 3.32 0.3 11.3
Turkey 0.1232 16.25 37.46 1.24 11.4 366.1 0.1847 19.60 17.46 3.11 0.1 7.3
Europe
Developed
Germany 0.0047 3.32 2.26 0.59 0.2 4.0 0.0322 8.45 7.47 1.55 0.2 6.1
Switzerland 0.0044 3.36 2.53 0.65 0.3 4.2 0.0346 7.38 7.12 1.22 0.2 7.3
United Kingdom 0.0050 2.51 2.01 0.48 0.0 4.1 0.0267 5.72 5.74 1.09 0.2 5.9
All statistics are based on daily returns (log-returns multiplied by a factor 100 for clarity). Exchange rates are in local currency over USD. Sample is drawn from 3 July 1995
through 29 July 2005, totalling 2630 observations for each series. All time-series of returns were found to be non-normal based on the JarqueeBera normality test.
a
Czech Republic stock market data is available from 1 January 1996.
b
Data for Russia is available from 7 March 1996 for the currency market and from 4 February 1997 for the stock market.
534
P.
Cumperayot
et
al.
/
Journal
of
International
Money
and
Finance
25
(2006)
528e550

eventually generate a series of out-of-sample volatility predictions ranging from 1 January 1996
through 29 July 2005.
Before estimating the GARCH model, we first determine the specification of the mean equa-
tion with the following nested sequence of tests:
1. An LM-test is used to test the presence of first order serial correlation in the returns.
2a. If the null hypothesis of no serial correlation is rejected, we estimate an AR(1) model with
an additional Monday dummy:
rt ¼ c0 þ c1Mont þ c2rt1 þ 3t ð3Þ
where rt is the daily currency (stock) market return on day t, 3t is the residual and Mont is
a dummy that equals 1 on Mondays only, in order to capture the weekend effect.9
2b. If the null hypothesis of no serial correlation cannot be rejected, we estimate the following
constant mean equation with a Monday dummy:
rt ¼ c0 þ c1Mont þ 3t ð4Þ
3. We test the hypothesis c1 ¼ 0, using a t-test based on Newey and West (1987) standard er-
rors, in order to correct for any heteroscedasticity and remaining autocorrelation in the error
terms. If the null hypothesis c1 ¼ 0 cannot be rejected, the Monday dummy Mont is dropped
from the mean equation.
Depending on the results of the nested tests, the coefficients c1 and c2 of the mean Eq.
(3) are restricted to zero and the restricted mean equation is then re-estimated. Next, we test
for the presence of conditional heteroscedasticity in the residuals with the ARCH-LM test
(Engle, 1982). If the null hypothesis of no first-order ARCH cannot be rejected, the stan-
dard deviation of the residuals is used as the predicted volatility for the following 5 work-
ing days.
If the null hypothesis of no first-order ARCH can be rejected, we estimate the restricted
mean Eq. (3) together with the T-GARCH(1,1) conditional variance Eq. (5) below:
s2
t ¼ u þ a32
t1 þ g32
t1dt1 þ bs2
t1 þ 4Mont; ð5Þ
where st
2
is the conditional variance at day t, 3t is the residual of the mean equation, dt is equal
to 1 if 3t 0 and 0 otherwise. The coefficients of the volatility equation are u, a, g, b and 4.
We use the estimated T-GARCH model to predict volatility for the next 5 working days.
Note that the specification of the variance Eq. (5) allows for an asymmetric reaction to negative
and positive return shocks (if g s 0), based on Glosten et al. (1993). The variance equation
9
See French (1980) and Jaffe et al. (1989), amongst others, for the weekend effect in stock markets. See McFarland
et al. (1982) and Hilliard and Tucker (1992) for evidence of day-of-the-week effects in foreign exchange markets. In
order to keep the model parsimonious we only include a Monday dummy, instead of a dummy for every weekday.
535

includes a Monday dummy in order to take into account the impact of news over the weekend
(see Kiymaz and Berument, 2003).10
3.3. Conditional probability of extreme currency market returns
A good first impression of the linkages between markets in times of stress may be gained by
estimating the conditional probability of an extreme event, given the occurrence of an extreme
event in another market or region. Before we estimate the conditional probabilities, we first
need to select the tail probability a for the definition of extremes. We choose a ¼ 5%, similar
to Bae et al. (2003). The use of a 5% threshold can be defended given the large sample of daily
return observations, implying the empirical distribution will typically contain a large number of
observations in the tail area. The 5%-threshold will also ensure sufficient observations are avail-
able to estimate the probit models and the conditional probabilities. For example, if we were to
use a ¼ 1%, then the probit model estimations would be based on just 25 extreme events. To
investigate the sensitivity of our results to the value of a, in Section 4.3 we will also estimate
the probit models with event dummies based on a ¼ 2.5% and show that the results are robust
to changes in the threshold level.
As the initial 130 returns in the sample (3 July 1995 through 29 December 1995) are used to
generate the first out-of-sample volatility forecast, the probit model estimations in Section 4 are
based on 2500 daily observations per country from 1 January 1996 through 29 July 2005 (save
for Russia and the Czech Republic). For the sake of consistency and comparability, we use the
same sample size in this section for estimating the conditional probabilities. Table 2 shows the
conditional probability of an extreme currency depreciation (or devaluation), i.e. yt,i
c
¼ 1, for
a ¼ 5%. The probabilities in columns 3e5 of Table 2 condition on the occurrence of the fol-
lowing events:
ys
t;i ¼ 1: extreme local stock market return on day t in country i
yc
t1;i ¼ 1: extreme currency return on the previos day t 1 in country i
ys
t1;i ¼ 1: extreme local stock market return on the previous day t 1 in country i
Bold font is used in Table 2 to indicate that a c2
-test rejects the null hypothesis of indepen-
dence between the two events at the 5% significance level. Since the unconditional probability
of each extreme event is by definition equal to a ¼ 5%, the c2
-test essentially tests the null hy-
pothesis that the conditional probability is equal to 5%.11
The results in the third column of
Table 2 indicate that extreme currency and stock market returns are not independent events
in most countries, except in Argentina, Canada, United States, India, Hungary, Poland, Ger-
many and the United Kingdom. Interestingly, the conditional probability of an extreme
10
Overall, the nested tests and model estimations are repeated 21,726 times. To avoid computational infeasibility,
a fairly general and parsimonious model specification was selected, with the aim of fitting the basic characteristics
of the data. Higher-order autocorrelation terms in both the mean equation and the variance equation had to be ignored
for this reason. We also estimated a bivariate GARCH model for the exchange rate and stock return volatility of a num-
ber of markets, but the forecasted volatilities were very close to those obtained from the univariate models (correlation
of 95% or more).
11
The null hypothesis of the c2
-test is: P[A X B] ¼ P[A]P[B], which is equivalent to P[AjB] ¼ P[A]. Since in this par-
ticular case P[A] ¼ P[B] ¼ 5% by definition, the null hypothesis is equivalent to P[AjB] ¼ 5%. We have also bootstrap-
ped 95% confidence intervals for a number of probabilities in the table, leading to similar results as the c2
-test for
rejection of the null hypothesis P[AjB] ¼ 5%.

Table 2
Conditional probability of an extreme currency return
Region Country i Country stock and currency
extreme event dummies
Regional currency market extreme
event dummy: xdt1,j
c
¼ 1
Regional stock market extreme event
dummy: xdt1,j
s
¼ 1
yt,i
s
¼ 1 yt1,i
c
¼ 1 yt1,i
s
¼ 1 j ¼ America j ¼ Asia j ¼ Europe j ¼ America j ¼ Asia j ¼ Europe
America
Emerging
Argentina 5 38 13 8 0 3 2 2 2
Brazil 14 23 10 23 0 11 11 4 11
Chile 11 19 9 7 8 11 3 5 4
Mexico 18 13 19 10 10 9 17 14 15
Peru 11 12 8 12 13 7 17 14 12
America
Developed
Canada 6 10 10 6 4 13 12 5 11
United States 8 13 6 5 14 9 8 9 9
Asia Australia
Emerging
India 7 23 10 3 16 7 8 13 6
Indonesia 26 31 17 6 30 4 10 16 7
Korea 23 29 18 10 28 7 16 17 12
Malaysia 34 40 22 2 41 1 10 21 7
Philippines 22 29 15 7 22 4 13 16 10
Taiwan 16 21 8 8 21 11 16 13 14
Thailand 22 30 14 7 28 4 12 19 13
Asia Australia
Developed
Australia 12 7 6 5 7 10 12 11 11
Japan 10 7 6 7 12 6 10 12 11
New Zealand 9 8 8 4 8 9 9 12 11
Singapore 19 16 12 9 26 7 15 14 12
Europe
Emerging
Czech Republic 9 12 11 6 8 6 6 11 8
Hungary 3 12 6 7 2 8 4 5 8
Poland 8 11 9 6 6 10 4 8 9
Russiaa
11 40 10 7 1 10 30 16 24
Turkey 15 20 10 8 1 8 3 3 4
Europe
Developed
Germany 2 9 6 6 0 5 6 5 7
Switzerland 1 7 6 5 3 4 10 7 11
United Kingdom 4 11 8 3 3 7 3 3 3
All numbers in the table are conditional probabilities, displayed as percents (%), i.e. multiplied by 100. Numbers in bold indicate the null hypothesis of independent events can
be rejected at the 5% level. Sample is from 1 January 1996 through 29 July 2005 (2500 observations).
a
Data for Russia is available from 7 March 1996.
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Finance
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(2006)
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currency depreciation ( yt,i
c
¼ 1) given a stock market downturn ( yt,i
s
¼ 1) ranges from 16% to
34% in countries that were directly affected by the Asian crisis, i.e. Indonesia, Korea, Malaysia,
Philippines, Taiwan, Thailand and Singapore.
The fourth column of Table 2 shows that the conditional probability of an extreme currency
return is positively related to the occurrence of an extreme currency return on the previous day
in all emerging markets and most developed markets. The conditional probability exceeds 20%
for Argentina, Brazil, all emerging Asian countries, Turkey and Russia. Hence, extreme cur-
rency depreciations tend to persist for a number of days, especially in countries that have ex-
perienced a currency crisis. The fifth column of Table 2 shows that the conditional probability
of an extreme currency return is also positively related to the occurrence of an extreme stock
market return on the previous day in most emerging markets.
In order to investigate the impact of region-wide extreme events, we define the variable
xt1,j
c
as a counter of the number of extreme currency events in region j over the previous
10 days, where j denotes one of the three regions, America, Asia Australia or Europe:
xc
t1;j ¼
X
i˛regionðjÞ
X
t10
k¼t1
yc
k;i ð6Þ
In order to indicate whether currency markets in region j are in turmoil, we define the
dummy variables xdt1,j
c
¼ I(xt1,j
c
nj,a
c
), where I($) is the indicator function and nj,a
c
is the
a%-percentile of the right tail of the counter variable. We choose nj,a
c
such that the dummy vari-
able xdt1,j
c
is equal to 1 for approximately a ¼ 5% of the observations in the sample.
Columns 6e8 of Table 2 show the conditional probability of an extreme currency event in
country i, conditional on a regional extreme currency event. Currency turmoil in America dur-
ing the 10 preceding days increases the probability of an extreme currency event on day t in
countries in America itself and in Korea, Taiwan, Singapore and Turkey. Periods of currency
turmoil in Asia have a large impact mainly within the region itself. Interestingly, a period of
currency turmoil in Asia reduces the probability of an extreme depreciation on the following
day in Argentina, Brazil, Russia, Turkey and Germany. This could be evidence of capital flows
from Asia to other markets around the world during the Asian crisis.
Lastly, we use regional stock market turmoil as the conditioning event. Similar to Eq. (6), we
define the variables xt1,j
s
to count the number of extreme stock market declines in each region
j over the past 10 days. The corresponding dummy variable is xdt1,j
s
¼ I(xt1,j
s
nj,a
s
), with
nj,a
s
the a%-percentile of the right tail and a ¼ 5%. Table 2 shows that a period of stock market
turmoil in America increases the probability of extreme currency events in most countries
within the region itself, in nearly all countries in Asia Australia, and in Russia and Switzer-
land. Interestingly, the stock markets of America have a stronger impact on extreme currency
events in Asia Australia than the currency markets of America. The impact of European
stock market turmoil on the exchange rate depreciation probabilities is very similar to the re-
sults for stock market turmoil originating from America, suggesting the presence of a common
factor.
3.4. Conditional probability of extreme stock market returns
We now focus our attention on the conditional probability of extreme stock market declines,
i.e. yt,i
s
¼ 1 with a ¼ 5%, shown in Table 3. First of all, the fifth column of Table 3 shows that

Table 3
Conditional probability of an extreme stock return
Region Country i Country stock and currency
extreme event dummies
Regional currency market extreme
event dummy: xdt1,j
c
¼ 1
Regional stock market extreme
event dummy: xdt1,j
s
¼ 1
yt,i
c
¼ 1 yt1,i
c
¼ 1 yt1,i
s
¼ 1 j ¼ America j ¼ Asia j ¼ Europe j ¼ America j ¼ Asia j ¼ Europe
America
Emerging
Argentina 5 9 16 10 8 11 16 12 16
Brazil 14 5 22 9 14 9 23 18 21
Chile 11 8 22 10 13 10 18 17 20
Mexico 18 12 15 8 9 10 13 16 18
Peru 11 6 15 3 6 8 17 14 15
America
Developed
Canada 6 8 10 6 7 10 14 14 16
United States 8 6 10 12 3 7 13 10 15
Asia Australia
Emerging
India 7 9 14 4 9 8 9 9 9
Indonesia 26 21 13 10 14 5 18 14 19
Korea 23 12 10 6 18 3 12 12 9
Malaysia 34 24 26 9 22 4 17 21 18
Philippines 22 19 16 11 13 6 18 15 17
Taiwan 16 9 10 10 8 6 12 8 11
Thailand 22 14 13 7 14 4 10 12 11
Asia Australia
Developed
Australia 12 7 11 7 12 7 17 10 15
Japan 10 3 7 6 8 4 16 8 15
New Zealand 9 10 9 6 11 7 18 13 18
Singapore 19 19 16 12 16 7 19 16 18
Europe
Emerging
Czech Republic 9 7 11 10 5 10 16 11 17
Hungary 3 6 17 6 9 9 23 16 24
Poland 8 11 15 7 10 5 23 12 22
Russiaa
11 13 18 2 12 8 21 17 20
Turkey 15 2 12 5 8 7 18 13 20
Europe
Developed
Germany 2 4 14 13 6 10 17 13 20
Switzerland 1 6 15 10 6 7 19 13 21
United Kingdom 4 2 14 13 4 10 19 12 21
All numbers in the table are conditional probabilities, displayed as percents (%), i.e. multiplied by 100. Numbers in bold indicate the null hypothesis of independent events can
be rejected at the 5% level. Sample is from 1 January 1996 through 29 July 2005 (2500 observations).
a
Data for Russia is available from 4 February 1997.
539
P.
Cumperayot
et
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Journal
of
International
Money
and
Finance
25
(2006)
528e550

the conditional probability of an extreme stock market event is positively related to the occur-
rence of an extreme stock market event on the previous day in all markets except Japan. Hence,
extreme stock market declines typically foreshadow more extreme declines. The fourth column
of Table 3 shows that the conditional probability of an extreme stock market decline is posi-
tively related to the occurrence of an extreme currency depreciation on the previous day in
all emerging Asian countries and in Argentina, Mexico, New Zealand, Singapore, Poland
and Russia. The lagged cross-market relationship tends to be weaker than the relation to an ex-
treme stock market return on the preceding day, except for a small number of countries in Asia
(probably due to the effect of the Asian crisis).
Columns 6e8 of Table 3 show the conditional probability of an extreme stock market de-
cline in country i, conditional on one of the three regional currency turmoil indicator variables
being equal to one. Periods of currency turmoil in Asia increase the probability of stock market
declines within the region itself and in eight emerging markets elsewhere. Here we probably
again see the influence of the Asian crisis, which may have led investors to reduce positions
in other emerging stock markets. Periods of currency turmoil in American and Europe have im-
pact on a limited number of stock markets across the globe, with the distinction that American
currency turmoil mainly affects emerging markets.
Columns 9e11 of Table 3 show that stock market turmoil during the 10 preceding days in
any region of the world significantly increases the probability of further declines in nearly every
stock market. This result illustrates the strong interconnection of stock markets in times of
stress, regardless of the origins of the turmoil. The probabilities in the columns for America
(column nine) and Europe (column eleven) are again highly similar, suggesting the presence
of a common risk factor.
4. Empirical results
In this section we present the estimation results for the simultaneous equations probit models
presented in Section 2. First, we select a set of explanatory variables X1t,i (X2t,i) to be used in
Eqs. (1) and (2), which also serve as the set of instruments in the two-stage estimation proce-
dure. The forecasted conditional volatility on day t is our first choice. Periods of high condi-
tional volatility are expected to lead to an increased probability of extreme events. We use
the out-of-sample forecast of conditional volatility of the currency return in country i on day
t (denoted by st,i
cf
) as an explanatory variable in Eq. (1). Similarly, we use the forecasted con-
ditional volatility of the local stock market return in country i on day t (denoted by st,i
sf
) as an
explanatory variable in Eq. (2).
The results in Tables 2 and 3 have shown that the probability of an extreme event increases
greatly if an extreme event also occurred on the previous day. An extreme event on the previous
day will usually also lead to an increase of forecasted volatility, based on the GARCH model.
However, in some countries and over certain periods, constant volatility could not be rejected
and the volatility forecast does not react to recent events. We therefore include the lagged ex-
treme event dummies ( yt1,i
c
and yt1,i
s
) as explanatory variables in Eqs. (1) and (2) in order to
investigate whether the lagged event dummies have any additional explanatory power beyond
the forecasted conditional volatility variable.
Finally, we add the total number of extreme currency events during the previous 10 days
(xt1,j
c
) in each of three regions America, Asia Australia and Europe as explanatory variables
to Eq. (1). Similarly, we add the total number of extreme stock market events during the previous

10 days (xt1,j
s
) in the three regions as explanatory variables to Eq. (2). We add these explanatory
variables to control for the impact of region-wide extreme events, which were shown to affect
the probability of extreme depreciation and stock market declines in Tables 2 and 3.12
We do
not include both sets of regional variables in each of the two Eqs. (1) and (2), since too
many identical explanatory variables in the system of equations could lead to identification
problems.
4.1. Currency market extremes
Table 4 displays the estimation results for Eq. (1), showing the estimated marginal effect for
each explanatory variable and the P-value for the estimated coefficient. The marginal effect
measures the estimated change in the probability of an extreme currency depreciation, given
a small change in the explanatory variable under consideration. The results show that an in-
crease in forecasted currency volatility has a significant positive effect on the probability of
an extreme event in 18 out of 26 currency markets. The conditional volatility variable does
not subsume the explanatory power of the lagged extreme currency event dummy, which is sig-
nificantly positive for 12 of the 26 countries (11 of these are emerging markets). Hence, ex-
treme currency depreciations tend to be clustered in emerging markets.
The occurrence of an extreme event on the stock market significantly increases the proba-
bility of an extreme currency depreciation on the same day (simultaneous events) in Argentina,
Malaysia, Thailand, Russia, Australia and Switzerland at the 5% level. For Brazil and Turkey
the marginal effect is 2.6% and the P-value less than 6.5%. Interestingly, the list includes a num-
ber of emerging markets that went through a severe currency crisis during the sample period.
These results could be interpreted as support for the hypothesis that emerging markets are af-
fected by changes in portfolio flows and expectations of foreign investors, as strong declines in
the equity market also place the local currency under pressure. An alternative explanation is
that in countries with a fixed or managed exchange rate the extreme stock market decline might
have reflected expectations of a devaluation just before the event actually took place.
With respect to the impact of region-wide extreme events, the results in Table 4 show that an
increase in the number of extreme currency events during the previous 10 days in the region
America leads to an increased probability of further extreme currency events in Argentina, Bra-
zil and Turkey. On the other hand, an increase of the American regional variable leads to a de-
creased probability of extreme events in Indonesia, Malaysia, the Philippines and Thailand. An
increase of the number of extreme currency events in Asia Australia significantly increases
the chance of extreme depreciation in all emerging Asian countries, whereas it reduces the
probability of extreme currency events in Argentina, Brazil, Russia, Turkey and Germany. Cur-
rency turmoil in Europe mainly increases the probability of further extreme events in developed
markets, namely Australia, Japan, New Zealand, Germany and the United Kingdom.
Overall, we find little evidence in favor of spill over of currency turmoil from one region to
another. On the contrary, after periods of increased currency turmoil in either America or Asia
Australia, a significant number of countries outside these two regions exhibit a decreased risk
of extreme currency depreciation.
12
Ideally, we would also like to add the contemporaneous number of extreme events in the three regions to the model’s
equations, but this would lead to an endogenous link between the extreme event dummies of all countries. The resulting
multivariate simultaneous equations probit model with 52 endogenous variables is too complex to estimate using current
econometric and computational tools. We leave this issue for future research.
541

Table 4
Estimation results for Eq. (1): currency market
Country st,i
cf
Regional extreme events xt1,j
c
j ¼ America j ¼ Asia j ¼ Europe yt1,i
c
yt1,i
s
yt,i
*s
R2
Argentina 0.3 0.3 L0.4 0.0 4.6 0.9 3.3 26.5
0.2 0.0 0.0 85.9 0.0 44.6 0.1
Brazil 2.6 0.4 L0.3 0.0 1.5 0.5 2.6 17.1
0.0 0.0 0.3 75.7 27.7 76.5 6.1
Chile 9.9 0.0 0.1 0.2 3.9 6.8 L6.1 10.8
0.0 94.9 17.1 8.2 3.0 0.7 0.3
Mexico 4.8 0.2 0.0 0.1 0.7 5.8 1.2 8.0
0.0 6.6 54.2 54.5 66.9 0.0 45.0
Peru 30.3 0.2 0.1 0.1 1.2 0.6 0.6 11.0
0.0 10.8 6.2 51.7 42.8 74.7 65.6
Canada 21.6 0.1 0.1 0.1 0.1 4.4 0.0 8.5
0.0 33.0 40.6 25.2 92.6 0.2 97.0
United States 23.6 0.1 0.2 0.1 2.9 0.5 0.4 4.9
0.3 51.3 0.0 44.5 4.7 80.0 76.5
India 9.4 0.1 0.2 0.2 4.4 4.0 1.7 11.8
0.0 42.2 0.2 16.0 0.2 2.6 34.6
Indonesia 0.3 L0.3 0.3 0.0 3.6 1.7 1.9 19.3
13.8 3.3 0.0 75.2 0.7 16.5 11.5
Korea 0.6 0.1 0.2 0.0 4.6 2.4 1.7 17.1
5.5 51.7 0.0 92.8 0.0 5.6 14.5
Malaysia 0.3 L0.4 0.2 L0.2 0.2 0.3 1.3 41.3
22.7 0.0 0.0 0.0 70.0 59.8 2.9
Philippines 6.4 L0.2 0.2 0.0 2.8 0.8 1.6 19.8
0.0 2.3 0.0 62.1 1.3 47.3 16.5
Taiwan 1.8 0.0 0.3 0.2 3.1 0.2 0.8 11.2
1.2 72.7 0.0 2.1 2.2 92.4 61.5
Thailand 1.2 L0.3 0.2 0.0 2.9 0.2 3.9 21.3
1.9 1.9 0.0 87.5 1.3 88.7 0.1
Australia 3.6 0.2 0.1 0.3 0.0 2.1 4.0 3.1
18.8 24.1 16.3 0.9 98.9 32.9 3.1
Japan 7.4 0.1 0.2 0.2 1.1 0.0 0.9 5.4
0.0 47.3 0.0 4.4 54.9 99.9 59.5
New Zealand 4.8 0.1 0.1 0.4 0.3 1.6 1.3 3.9
2.2 40.0 20.2 0.1 84.3 33.3 39.6
Singapore 10.7 0.1 0.2 0.1 0.0 0.6 0.4 14.5
0.0 59.1 2.3 22.4 97.9 66.7 73.9
Czech Republic 3.7 0.1 0.1 0.1 3.8 3.6 1.4 2.5
6.8 50.8 7.4 67.5 2.3 4.7 45.6
Hungary 11.4 0.1 0.1 0.1 2.2 1.0 1.1 5.9
0.0 66.5 39.9 39.3 12.9 60.4 51.4
Poland 10.0 0.0 0.0 0.2 1.1 3.1 1.1 6.5
0.0 81.6 92.9 16.5 54.5 10.2 53.5
Russia 0.0 0.0 L0.2 0.1 6.4 L6.1 9.2 27.9
76.9 82.1 0.6 68.2 0.2 0.5 0.0
Turkey 0.5 0.2 L0.3 0.2 7.1 0.8 2.6 9.2
0.0 3.3 0.2 7.6 0.0 64.2 6.2

4.2. Stock market extremes
Table 5 summarizes the estimation results for Eq. (2) of the simultaneous equations probit
model, with the probability of an extreme stock market return as the dependent variable. An
extreme currency depreciation significantly decreases the probability of a simultaneous stock
market decline in Brazil, Canada, the United States and Switzerland. The results are in line
with the argument that currency depreciations might be good news for the stock market due
to increased export competitiveness. The significant positive link from currency depreciations
to stock market declines in Singapore might be explained by the fact that Singaporean exports
contain a large import component (Abeysinghe and Yeok, 1998).
For emerging markets we hypothesized that extreme currency depreciations (or devalua-
tions) are not necessarily positive events for the stock market, due to the potential negative im-
pact on domestic lenders with debt denominated in foreign currency and on the stability of the
banking system in general. Malaysia is the only emerging market with a significantly positive
contemporaneous link from currency extremes to stock market declines. Interestingly, the
lagged extreme currency event dummy is significantly positive for seven emerging markets.
This might indicate that the stock market needs some time to evaluate the impact on the econ-
omy of a strong currency depreciation.
Concentrating on the remaining explanatory variables in Table 5, we find that an increase of
forecasted conditional stock market volatility leads to a higher probability of an extreme stock
market decline in 19 out of 26 countries. The lagged stock market dummy is significantly pos-
itive in three emerging markets and Singapore, indicating clustering of extreme events in these
countries. Turning to the regional effects, we find that the number of extreme stock market de-
clines in both Asia Australia and Europe do not have an overall significant impact on the
probability of further extreme stock market declines.13
Stock market turmoil in America, on
the other hand, increases the probability of further extreme declines in 10 countries in the re-
gions Asia Australia and Europe. Thus, spillover of extreme stock market declines occurs,
but only from America to countries in Europe and Asia, which probably reflects the leading
role of the United States among the world’s stock markets.
Table 4 (continued)
Country st,i
cf
c
c
yt1,i
s
yt,i
*s
R2
Germany 3.8 0.1 L0.2 0.3 2.1 0.9 1.0 1.6
67.2 66.7 4.8 1.4 23.2 64.0 37.8
Switzerland 9.7 0.2 0.1 0.2 1.1 1.7 3.3 1.9
7.4 25.4 14.3 18.2 57.2 43.2 1.1
United Kingdom 30.2 0.3 0.0 0.4 1.6 4.0 L2.8 3.4
4.1 6.8 69.5 0.1 37.9 2.6 2.9
Estimation results for the equation yt,i
*c
¼ g1i yt,i
*s
þ b1i
0
X1t,i þ 31t,i. The matrix X1t contains the explanatory variables
listed in the top row of the table and a constant term. The first row of results for each country shows the estimated mar-
ginal effect of the explanatory variables, while the second row contains the corresponding P-value. All numbers dis-
played in the table are percents (%), i.e. multiplied by 100. Numbers in bold denote significance at the 5% level.
13
Based on a binomial distribution, there is a 13.9% (3.9%) probability that the null hypothesis of independence will
be rejected incorrectly for three (four) or more out of 26 countries.
543

Table 5
Estimation results for Eq. (2): stock market
Country st,i
sf
s
s
yt1,i
c
yt,i
*c
R2
Argentina 2.3 0.2 0.0 0.0 1.7 2.3 0.0 6.9
0.0 31.6 92.6 70.2 40.0 13.4 96.5
Brazil 0.7 0.1 0.2 0.1 0.4 5.6 L2.0 11.8
24.1 50.7 5.3 32.3 86.3 0.0 4.7
Chile 3.4 0.1 0.1 0.0 2.2 5.4 0.1 10.0
1.9 33.7 11.6 71.3 25.2 0.0 95.0
Mexico 2.6 0.0 0.2 0.1 2.5 3.8 2.7 9.0
0.8 86.2 0.7 60.3 13.1 4.2 15.2
Peru 4.3 0.4 0.0 0.1 0.8 1.7 2.0 7.3
0.0 0.7 96.0 38.2 66.9 25.6 8.0
Canada 4.4 0.2 0.1 0.3 2.6 1.3 L3.4 9.0
0.0 29.9 27.5 2.1 12.5 44.5 1.0
United States 5.9 0.2 0.2 0.2 2.8 0.7 L4.9 8.7
0.0 17.1 12.3 18.0 20.1 71.4 1.6
India 3.2 0.3 0.0 0.0 2.9 3.1 0.8 5.8
0.0 6.6 66.8 74.4 21.3 5.0 63.1
Indonesia 1.1 0.2 0.0 0.2 4.5 0.5 1.1 11.9
0.4 9.5 92.2 9.6 1.2 71.2 42.6
Korea 2.6 0.0 0.1 0.0 1.4 1.1 2.1 9.3
0.0 94.4 16.8 89.4 49.1 47.3 10.9
Malaysia 0.6 0.2 0.2 0.1 1.3 3.4 1.6 17.5
5.3 12.9 1.8 50.7 32.6 0.2 0.2
Philippines 1.5 0.3 0.1 0.1 3.9 1.5 0.9 10.7
1.1 1.7 17.5 46.5 1.3 27.7 38.7
Taiwan 2.9 0.4 0.0 0.1 1.0 0.1 0.4 4.4
0.3 0.4 87.8 47.5 63.5 95.9 82.7
Thailand 2.1 0.4 0.0 0.1 0.5 1.5 1.9 7.6
0.0 0.5 79.6 19.5 77.7 30.5 11.6
Australia 3.3 0.3 0.0 0.1 0.4 1.9 1.3 4.9
23.4 5.7 79.6 23.4 82.5 24.9 56.5
Japan 3.5 0.4 L0.3 0.2 3.2 0.5 1.7 5.4
1.6 0.4 1.1 18.4 14.2 77.0 33.5
New Zealand 2.2 0.4 0.1 0.0 2.6 1.4 0.4 7.6
7.8 0.9 29.0 94.2 8.9 43.4 81.7
Singapore 1.1 0.4 0.0 0.0 3.8 1.1 2.1 13.0
12.7 0.4 66.8 78.3 0.4 42.2 4.9
Czech Republic 2.8 0.4 0.2 0.1 1.2 0.8 4.7 5.9
3.1 1.7 16.8 66.4 65.8 74.4 24.1
Hungary 0.9 0.5 0.0 0.0 1.6 3.0 2.1 9.4
11.1 0.2 79.2 88.9 39.3 4.9 14.0
Poland 0.0 0.5 0.1 0.1 3.9 3.5 1.5 8.0
98.9 0.2 38.0 23.0 1.7 2.0 32.3
Russia 1.0 0.1 0.3 0.1 0.2 3.2 0.1 11.5
0.1 41.8 0.2 48.9 95.4 1.8 98.2
Turkey 1.9 0.0 0.1 0.2 L5.3 0.9 0.5 7.1
0.0 78.3 23.2 8.4 4.9 54.7 71.0

4.3. Robustness of results
In order to verify the robustness of the estimation results, we will now discuss how the re-
sults change when we adjust a number of the model’s parameters. From Section 3 onwards we
have used the 5%-percentile of the return distribution as the threshold for extreme stock market
declines and the 95%-percentile as the threshold for extreme currency depreciations (i.e.
a ¼ 5%). This might raise the concern that we include a number of observations that are not
truly extreme. To investigate this issue, we have re-estimated the probit models with extreme
event dummies based on a tail probability of 2.5% (i.e. a ¼ 2.5%).
The main effect on the results in Table 4 for the currency market by ‘‘going deeper into the
tail’’ is that the contemporaneous link from stock market declines to extreme currency events be-
comes stronger for emerging markets that went through a crisis. (Results are not presented to save
space, but are available on request from the authors.) The coefficient is now significantly positive
at the 5% level for Argentina, Korea, Malaysia, Taiwan, Thailand, Russia, Turkey and Switzer-
land. We also find that regional spillover effects become weaker, i.e. the estimated coefficients are
smaller and less significant, but the overall pattern is still similar to that in Table 4.14
The effect of changing a to 2.5% on the results for the stock market equations are as follows:
The contemporaneous link from currency depreciation to stock market declines is negative and
significant at the 5% level for Brazil, Peru, Canada and Hungary. The estimated marginal ef-
fects for US and Switzerland are still negative (1.6% and 1.3%), but no longer significant.
The link is positive and significant at the 5% level for Indonesia, Korea, Malaysia and Singa-
pore. Hence, we again find some evidence in favor of the ‘‘increased exports’’ effect of cur-
rency depreciations and limited evidence in favor of the ‘‘increased risk’’ effect. Finally,
stock market turmoil in America still increases the probability of further extreme declines in
many countries (11 out of 26), while the regional variables for Asia Australia and Europe
are insignificant overall (as in Table 5).
A second parameter of the model is the number of lags used for the regional extreme event
counter variables, which we have so far set at 10 working days. The argument for using lagged
Table 5 (continued)
Country st,i
sf
s
s
yt1,i
c
yt,i
*c
R2
Germany 4.5 0.2 0.1 0.0 0.3 1.1 3.7 14.4
0.0 8.5 15.0 89.9 89.3 47.1 15.4
Switzerland 4.9 0.3 0.1 0.1 0.8 0.1 L5.8 11.4
0.0 9.5 21.7 71.8 69.1 97.6 3.1
United Kingdom 4.5 0.4 0.1 0.1 3.8 0.3 0.6 12.6
0.0 0.3 12.2 39.6 12.3 82.2 75.9
Estimation results for equation yt,i
*s
¼ g2i yt,i
*c
þ b2i
0
X2t,i þ 32t,i. The matrix X2t contains the explanatory variables listed in
the top row of the table and a constant term. The first row of results for each country shows the estimated marginal effect
of the explanatory variables, while the second row contains the corresponding P-value. All numbers displayed in the
table are percents (%), i.e. multiplied by 100. Numbers in bold denote significance at the 5% level.
14
Smaller marginal effects and less significant coefficients is what we would expect to find a priori, as we decrease the
number of extreme events per series from 125 (5%) to 63 (2.5%).
545

regional information is that during crises investors might need time to assess the potential effect
of extreme events in the region on other, related, countries. Of course, the 10-day period is
somewhat arbitrary, so we also estimated two sets of probit models with the regional variables
defined as the sum of extreme events over respectively the last 5 and the last 15 days. The re-
sults reveal that the impact of changing the lag window of the regional extreme event counters
is very small and does not lead to any material changes in the conclusions (results available on
request).15
Finally, we have also estimated a version of the model without the regional extreme event
counters as explanatory variables, focusing strictly on the link between extreme events within
countries. The main observation is that in these models the link from extreme stock market de-
clines to currency depreciations becomes stronger: the coefficient is positive and significant at
the 5% level for Argentina, Brazil, Korea, Malaysia, Thailand, Russia, Turkey, Australia and
Switzerland. We conclude that in some cases extreme local stock market declines reflect infor-
mation about regional events, as the link from stock to currency markets is somewhat weaker in
Table 4 (after including the regional extreme event counters).
We also observe that the estimated coefficients of the conditional volatility variable increase
considerably after dropping the regional extreme event variables from the model.16
These re-
sults suggest that the conditional volatility of the local currency market (or stock market) re-
flects information originating from other countries. To illustrate this effect, Table 6 shows
the correlations between the forecasted conditional volatility for day t for the local currency
market (stock market) and the number of extreme events in the currency markets (stock mar-
kets) in each of the three regions during the last 10 days (from t 1 to t 10). Panel A of Table
6 demonstrates that the currency market volatility of a country is typically positively correlated
with the number of extreme currency events within its own region. We also see some evidence
of a positive relationship between regional currency turmoil and currency volatility in countries
outside the region, especially in developed markets. However, we again observe a negative re-
lationship between regional currency turmoil in Asia and the level of currency market volatility
in a number of countries in America and Europe.
The results in Panel B of Table 6 for the association between local stock market volatility
and regional stock market extreme events are much more homogeneous: all correlations are
positive and more than half are larger than 50% (the minimum correlation is 22% and the max-
imum is 77%). Stock markets are integrated to such an extent that an increase of the number of
extreme events in any region has a positive impact on the level of local stock market volatility
around the globe.
5. Conclusions
In this paper we study the empirical link between extreme returns in stock and currency mar-
kets and the pattern of spillover within and between regions. We find that simultaneous extreme
currency market depreciations and extreme stock market declines occur more frequently than
15
We also recalculated the conditional probabilities in Tables 2 and 3 and found that these results are insensitive to
changes in the lag window of the regional extreme event counters as well.
16
Four estimated coefficients for the currency volatility variable change from insignificant in Table 4 to significantly
positive, while seven coefficients for the stock market volatility variable in Table 5 change from insignificant to signif-
icantly positive.

might be expected under the assumption of independence in 17 out of 26 countries in our sam-
ple. In a number of emerging markets affected by the Asian crisis the conditional probability of
an extreme currency depreciation given a stock market downturn on the same day is more than
20% (whereas the unconditional probability is 5%).
We estimate a simultaneous equations probit model for the relationship between extreme
event dummies for the currency market and the stock market. For a number of emerging coun-
tries that went through a severe crisis we find that an extreme stock market decline significantly
increases the probability of extreme currency depreciation on the same day. A potential expla-
nation might be increased equity flows out of the country, which then in turn depress the cur-
rency. An alternative explanation is that the stock market might have anticipated a change in
a pegged or managed currency just before it occurred.
Table 6
Correlation between local volatility and regional extreme event counters
Region Country i A e Currency market B e Stock market
Correlation between volatility
st,i
cf
and regional extreme
events xt1,j
c
Correlation between volatility
st,i
sf
and regional extreme
events xt1,j
s
j ¼ America j ¼ Asia j ¼ Europe j ¼ America j ¼ Asia j ¼ Europe
America
Emerging
Argentina 23 13 2 52 27 42
Brazil 48 14 18 77 61 65
Chile 50 1 28 63 50 54
Mexico 42 23 17 73 60 66
Peru 24 17 1 42 23 29
America
Developed
Canada 40 7 38 66 50 61
United States 38 33 41 66 48 69
Asia Australia
Emerging
India 6 36 10 31 37 22
Indonesia 8 72 3 43 57 40
Korea 5 58 4 41 58 37
Malaysia 19 34 13 53 66 57
Philippines 16 32 19 44 50 46
Taiwan 13 48 4 36 34 32
Thailand 3 67 1 38 61 30
Asia Australia
Developed
Australia 27 8 45 61 67 65
Japan 17 33 24 44 48 45
New Zealand 22 12 39 59 62 55
Singapore 18 79 6 58 71 52
Europe
Emerging
Czech Republic 26 17 41 52 33 61
Hungary 31 18 42 62 50 61
Poland 34 1 59 62 51 63
Russiaa
9 9 19 55 52 57
Turkey 11 9 17 36 28 48
Europe
Developed
Germany 21 19 21 52 37 67
Switzerland 16 5 16 60 41 74
United Kingdom 31 10 31 50 37 62
All numbers in the table are correlations, displayed as percents (%), i.e. multiplied by 100. Sample is from 1 January
1996 through 29 July 2005 (2500 observations).
a
For Russia exchange rate data is available from 7 March 1996 and stock market data from 4 February 1997.
547

An extreme currency market depreciation reduces the probability of an extreme stock market
decline significantly in the United States, Canada, Switzerland and Brazil. These results are in
line with the hypothesis that a currency depreciation improves export competitiveness and stim-
ulates the local economy, leading to a positive reaction in the stock market. For emerging coun-
tries with large amounts of unhedged foreign debt and/or a weak banking system we
hypothesized that a large currency depreciation might increase the probability of simultaneous
stock market decline due to increased economic and financial risk. The empirical
support for the latter effect is weak. A potential explanation is that the two hypothesized effects
of exchange rate depreciation are of opposite sign and could therefore partially offset each
other.
Overall, we find some evidence of spillover of extreme currency events within regions, but
limited evidence that extreme events in currency markets spillover from one region to another.
Similar results are reported by Hartmann et al. (2003b), based on multivariate EVT and weekly
data. Interestingly, a significant number of countries outside the Asian region will have a lower
risk of extreme depreciation after periods of increasing extreme currency events in Asia Aus-
tralia. We find a similar negative relationship between currency turmoil in the region America
and the probability of extreme depreciation in four Asian markets. These negative links
across regions could be a manifestation of international capital flight from one region to the
other during periods of crisis. On the global stock markets, spill overs of extreme events
from one region to another do occur, but only from America to countries in Europe and
Asia. This relationship is also reported by Bae et al. (2003), and most likely confirms the lead-
ing role of the United States among the world’s stock markets (in line with results in Phylaktis
and Ravazzolo, 2005).
Our results emphasize that governments and central banks concerned about the spillover of
extreme currency events should probably pay more attention to regional currency markets than
global markets. Increased local currency market volatility, extreme local stock market declines
and an increasing number of regional extreme currency events are useful indicators of increas-
ing currency crisis risk in emerging markets. Two useful indicators for the prediction of ex-
treme stock market declines are increasing local stock market volatility and an increasing
number of extreme market declines in the region America.
Acknowledgements
Phornchanok Cumperayot is grateful to the APEC Finance and Development Program for
financial support. The authors would like to thank two anonymous referees, Fernando Perez
de Garcia Hidalgo and Kate Phylaktis for their comments on the paper. The paper has also
benefited from helpful suggestions made by participants at the 2005 Emerging Markets Finance
conference at Cass Business School, London. The authors are grateful to AEGON Asset Man-
agement for providing access to financial data and would like to thank Debbie Kenyon-Jackson
at ConLingua for her assistance in editing the final draft.
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