Download Exchange Rate Volatility and Democratization in Emerging Market

International Studies Quarterly (2003) 47, 203–228
Exchange Rate Volatility and
Democratization in Emerging Market
Countries
JUDE C. HAYS
University of Michigan
JOHN R. FREEMAN
University of Minnesota
HANS NESSETH
University of Minnesota
We examine some of the consequences of financial globalization for
democratization in emerging market economies by focusing on the
currency markets of four Asian countries at different stages of
democratic development. Using political data of various kindsFincluding a new events data seriesFand the Markov regime switching model
from empirical macroeconomics, we show that in young and incipient
democracies politics continuously causes changes in the probability of
experiencing two different currency market equilibria: a high volatility
‘‘contagion’’ regime and a low volatility ‘‘fundamentals’’ regime. The
kind of political events that affect currency market equilibration varies
cross-nationally depending on the degree to which the polity of a
country is democratic and its policymaking transparent. The results
help us better gauge how and the extent to which democratization is
compatible with financial globalization.
Much has been written in recent years about the economic benefits of globalization.
However, scholars have begun to question whether globalization, particularly the
internationalization of financial markets, is compatible with democracy. They have
expressed concern that global markets impose costs and constraints on governments in ways that limit the possibilities for democratic politics (Rodrik, 2000).1
Authors’ note: Earlier versions of this article were presented on panels at the 2001 meetings of the American
Political Science Association and the International Studies Association, and at research seminars at the University of
Colorado, New York University, the University of North Texas, and Yale University. We thank participants on these
panels and in these seminars for comments and criticisms. We especially appreciate the input of William Bernhard,
David Leblang, and Tom Willet. For the data used in gauging the impact of extragovernmental political behavior on
financial markets we thank Doug and Joe Bond.
1
Arguing by analogy to the Mundell-Flemming conditions for open economies Rodrik (2000) contends that,
given the pace and scope of international market integration, countries have to choose between a ‘‘golden straight
jacket’’ in which there is little room for government to maneuver and global federalism. Only the latter, in his mind,
allows for any meaningful form of mass politics or popular sovereignty over the economy. He calls this the ‘‘political
trilemma.’’
r 2003 International Studies Association.
Published by Blackwell Publishing, 350 Main Street, Malden, MA 02148, USA, and 9600 Garsington Road, Oxford OX4 2DQ, UK.
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Exchange Rate Volatility and Democratization in Emerging Market Countries
FIG. 1.
One of the most significant developments in the globalization of finance has been
the unprecedented increase in capital flows to developing countries (IMF, 1997).
Theoretically, these flows have been linked to contagion and volatility in the
financial markets of emerging market economies (Calvo and Mendoza, 2000). And
there is considerable anecdotal evidence that periods of financial instability are
triggered by political events in young and incipient democracies. Take Southeast
Asia, for example. Analysts are convinced that revelations about corruption in the
Estrada administration and uncertainty about Estrada’s ability to survive an
impeachment trial caused volatility in the Philippine peso, and conversely that the
smooth presidential transition from Estrada to Arroyo produced positive reactions
in the Philippine currency and stock markets.2 In the case of Indonesia, uncertainty
about the secession of provinces, the survival of the president, and other political
factors presumably were responsible for the ‘‘chaos’’ in the rupiah-dollar exchange
rate (Figure 1). This issue is important because ‘‘chaos’’ in foreign exchange
markets is costlyFcurrency volatility reduces trade, which diminishes national
income, and contributes to the outbreak of financial crises like those that swept
through Asia in l997Fand if domestic politics is a contributing cause of this
volatility, then there is a potentially serious tension between democratization in
emerging market countries and financial globalization.3
But is domestic politics responsible for currency market volatility in the emerging
market economies? The answer is not clear. Significant progress has been made in
understanding how uncertainty stemming from elections, legislative politics, and
other political variables affects the behaviors of currency, equity, and bond markets.
This research has shown that certain political institutionsFfor example, particular
kinds of electoral systemsFmitigate the effects of political uncertainty on global
markets in countries like Germany.4 But, to date, most of this work has been
2
See, e.g., Estrada Impasse, Far Eastern Economic Review, November 23, 2000, p. 80; and After the B Movie, A
New Main Attraction to Filipinos, The Economist, January 27, 2001, p. 37.
3
On the link between currency volatility and (reduced) trade see Rose (2000); see also Leblang (2001). The
relationship between trade and income (growth) is studied in such works as Frankel and Roemer (l999). The Asian
financial crisis had a devastating impact on several of the region’s economies. Annual growth rates in Thailand,
South Korea, and Indonesia went from above 5% before the crisis to below –5% in 1998 (Mishkin, 1999).
4
Representative of the burgeoning literature on politics and financial markets in countries in North America
and Europe are Roberts (l990), Alesina, Roubini, and Cohen (l997), Bernhard and Leblang (2001, 2002), Freeman,
Hays, and Stix (2000), Herron et al. (1999), Herron (2000a, 2000b), Leblang (2001), Leblang and Bernhard (2000b)
and Lin and Roberts (2001).
JUDE C. HAYS ET AL.
205
focused on long-standing democracies in North America and Europe rather than
on young and incipient democracies like those in the Philippines and Indonesia.
The research that has been done on young and incipient democraciesFfor
instance, the important book by Haggard (2000)Fargues that because of the
uncertainty they create about the government’s ability to generate mass support
and manage (cooperate with) its opposition, less democratic and opaque political
institutions produce more market uncertainty and hence their respective societies
experience comparatively greater social costs in financial crises. For example, the
costs of the crisis of l997 were greater in Indonesia than in South Korea and
Thailand because of the greater uncertainties created for markets by Indonesia’s
nondemocratic institutions. Unfortunately, Haggard and others (e.g., MacIntyre,
2001) focus mostly on this crisis period and do not analyze the continuous effect
domestic politics may have on currency markets and hence on the economy. Nor do
they characterize or establish empirically the causal mechanism that links political
uncertainty and currency market behavior in young and incipient democracies.5
These are the purposes of this article. Using political data of various
kindsFincluding a new events data seriesFand the Markov regime switching
model from empirical macroeconomics, we show that, in young and incipient
democracies, politics causes continuous changes in the probability of observing
different market equilibria (or regimes), one of which connotes high currency
volatility and contagion and hence, by implication, reduced trade, investment, and
national income. The kind of politics that affects currency market equilibration
varies cross-nationally depending on the degree to which a country is democratic
and its policymaking transparent. In young democracies like Thailand and South
Korea, which have relatively established democratic institutions and processes, the
probability of shifts to and from ‘‘contagion’’ regimes depends on familiar variables
like the degree of legislative support for the executive. On the other hand, when
democratic institutions are less developed and policymaking is more opaque, as in
Indonesia, the probability of shifts to harmful, volatile currency market equilibria
depends on extragovernmental politics such as riots and other forms of social
violence. Countries like the Philippines represent intermediate cases; both intraand extragovernmental politics affect the probability of experiencing harmful
currency market volatility in these countries.
The discussion is divided into three parts. The first part briefly reviews the
relevant political science and economic literatures. It finds in the former some
testable propositions about which political variables should affect market behavior
and identifies in the latter the Markov switching model as a useful framework to
study the impact of domestic politics on international currency markets. Using data
from four Asian countries at different stages of democratic development and the
model from economics, the propositions are evaluated in the second part of the
paper. The debate about the compatibility of globalization and democratization is
revisited in light of the findings in the conclusion.
Politics and Currency Markets
A handful of political scientists have studied the connections between domestic
politics and international financial markets, including currency markets. They have
used a variety of research designs for this purpose and their work provides valuable
insights into how currency traders respond to politics in young and incipient
democracies. For instance, Bernhard and Leblang (2002) show that politics
influences the relationship between spot and forward exchange rates. During
electoral campaigns, cabinet negotiations, and cabinet dissolutions, forward
5
The work of Haggard, MacIntyre, and others on the political economy of Asian and other developing
democracies is discussed in greater detail below.
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Exchange Rate Volatility and Democratization in Emerging Market Countries
exchange rates are a biased predictor of future exchange rates. Similarly, Frieden
(2002) found that electoral periods and indicators of the relative size of certain
sectors of the economyFindicative of the power of particular interest groups
Faffect average annual rates of depreciation and coefficients of variation in
exchange rates in fourteen OECD countries.6
Research on politics and security markets yields complementary findings.
Illustrative is Lin and Roberts’s (2001) demonstration that returns for certain
stocks were affected by expectations about the outcome of the March 2000
presidential election in Taiwan. Also, Pantzalis, Strangeland, and Tuttle (2000)
discovered that stock price movementsFpatterns of strongest abnormal returns
Foccur in the weeks immediately preceding but not following elections. This is
because in the final weeks of the campaign the resolution of political uncertainty
Fespecially in closed, or ‘‘less free’’ countriesFcauses stock prices to rise.7
The main implication of these findings is that currency traders continuously
monitor and react to information about the workings of young and incipient
democracies, particularly information about electoral politics and government
formation (maintenance). Electoral outcomes and the popularity of elected officials
affect the course of economic policy. Therefore, where political polls are conducted
regularlyFfor example, in the PhilippinesFthey will have a continuous impact on
currency trading and on the possibility of currency volatility. Who controls key
ministries also affects economic policy and so the information traders receive about
cabinet negotiations (survival) will also affect their behavior.
As regards the possibly mitigating effects of institutions, Freeman, Hays, and Stix
(2000) have highlighted the importance of electoral systems. They studied the
effects of political uncertainty on the currencies of four developed economies
FAustralia, Germany, United Kingdom, and the United StatesFand found that
their measures of political uncertainty helped to identify low and high volatility
exchange rate regimes in every case but Germany. They concluded that this was
due to the political constraints arising from German coalition politics, which, in
turn, were traced to Germany’s electoral system. Generalizing, they argue that the
proportional representation (PR) election systems associated with consensual forms
of democracy tend to buffer currency markets from the effects of political
uncertainty; where PR is used, information about elections and cabinet stability
does not affect currency market behavior.
By contrast, in majoritarian, winner-take-all democracies with first-past-the-post
electoral systems, elections are more likely to produce significant changes in
economic policy, which makes the effect of political uncertainty on their currency
markets more pronounced. To the extent that this familiar distinctionFconsensual
versus majoritarian (Lijphart, 1999)Fapplies to young democracies, our expectations are clear. Thailand and other young democracies that have PR electoral
systems should, like their older democratic counterparts (e.g., Germany), not
experience any impact of electoral politics and cabinet stability on currency
volatility.
6
Bernhard and Leblang (2002) use dummy variables to represent the effect of campaign periods and cabinet
politics. In a companion study, the same authors (Leblang and Bernhard, 2000) employ an EGARCH set-up using
dummy variables in the conditional variance expression. Among twenty-five elections, eight have effects on the
conditional variance in the log difference of the exchange rates. Lobo and Tufte (1998) report similar results for a
GARCH model of currency movements. Frieden (2002) uses the annual average change and the coefficient of
variation in the nominal exchange rate as his dependent variable and employs a simple, pooled cross-section, time
series linear regression model in his analysis.
7
Lin and Roberts (2001) regress individual stock returns on the daily Iowa Electronic Markets contract for Chen
in the weeks leading up to the March 2000 election and find that a number of stock returns are affected by this
political variable. Pantzalis et. al. (2000) argue that the asymmetry of information in closed (‘‘less free’’) political
systems means that elections resolve more uncertainty than in open (‘‘free’’) political systems, especially when the
incumbent is defeated.
JUDE C. HAYS ET AL.
207
But what of countries like Indonesia where democracy is just taking root? In
incipient democraciesFthat is, countries where democratic institutions are
emerging but the polity retains a significant degree of authoritarianismFthere is
a greater possibility of coups and democratic reversals. Hence political uncertainty
is often times of a different character and perhaps greater magnitude than in young
democracies where the political institutions are new but fully consolidated.
Research on these transition cases is more qualitative and suggestive than
quantitative and empirical. It suggests that the key determinants of market
uncertainty are the lack of (1) political transparency or ‘‘information symmetry’’
between currency traders and policymakers, and (2) government capacity to
generate mass support and manage its opposition.
Transparency means traders can be relatively certain about the content and aims
of economic policy, and therefore better able to anticipate macroeconomic
performance, than where policymaking is more opaque. Of course political
transparency is a key correlate of democracy so there should be a direct
relationship between the degree of democratic development and the amount of
currency market volatility we observe in countries’ exchange rates.8
The second factor has to do with governments’ abilities to implement their policies.
Opposition from parties not in the government and from the general population can
undermine the effectiveness of economic and other kinds of policies. Uncertainty
about the depth of such opposition makes it much harder for traders to anticipate
macroeconomic performance. Traders therefore are more susceptible to speculation
and contagion creating market volatility and financial crises (Haggard, 2000:51).
The implication is that, in more opaque, incipient democracies, social unrest
Fespecially riots and other forms of violent opposition to government policyFis
indicative of uncertainty about the content and effectiveness of government policy.
Where democracy is less developed as in Indonesia, electoral data and assessments
of cabinet stability may not have an impact on currency volatility; extragovernmental
factors like riots are more likely to be expressions of opposition to government
policy and therefore social unrest should be more likely to spawn currency
volatility. In contrast, electoral polls and intragovernmental variables such as those
that reflect the degree of legislative support for the executive will affect currency
volatility in young democracies like South Korea and Thailand; in these cases, social
unrest may not be indicative of opposition to government policies and hence it may
not have any impact on currency markets.9
These then are our general expectations about the way politics is linked to
currency market behavior within and across young and incipient democracies. But
what causal mechanism is at the heart of this linkage? Political science has few
answers to this question. The economics literature is more useful in this regard.
8
For instance, Haggard (2000:16ff) contends, ‘‘nontransparent political relationships between politicians and
particular firms contributed to the economic vulnerabilities [and risk associated with the South Asian financial crisis
of the late 1990s].’’ One of the main arguments of his book is that this crisis ‘‘showed the democracies [of that region]
to be resilient and has enhanced the cause of economic reform in the region. But . . . reforms will also require a
parallel process of deepening democracy by enhancing accountability and transparency of government’’ (3). Political
risk analysts also consider transparency and democracy to be less prone to market volatility; see, e.g., Howell
(2001:24, 230).
9
In a number of places in his book, Haggard (2000:48, 91, 168) stresses that nonelectoral or unconventional
forms of political participation like social violence deeply constrain governments’ abilities to cope with financial
crises. Again, he argues that Indonesia’s less democratically developed government was comparatively the least able
to cope with such participation. In contrast, in countries like South Korea, political parties openly and
constitutionally forged an agreement that reassured financial traders (51, 100ff).
Of course, it need not be the case that extragovernmental politics affects currency volatility in incipient
democracies only and intragovernmental politics affects currency volatility in young democracies only. The
differences across young and incipient democracies are likely to be differences of degree rather than kind. It is
entirely plausible that extra- and intragovernmental politics affects currency volatility in both young and incipient
democracies.
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Exchange Rate Volatility and Democratization in Emerging Market Countries
a. Economic Understandings of Currency Markets
Empirical research in international economics has produced a number of findings
about currency price dynamics. For example, currency returns are nonstationary
(unit root property), display periods of marked turbulence and quiescence
(clustering property), and exhibit extreme values more often than one would
expect if the series were normally distributed (fat tail property). (See DeVries,
1994.) A formalism that accounts for these properties is the Markov regimeswitching model (Hamilton, 1989; Engel and Hamilton, 1990). This model holds
that currency markets can be in two or more states. The states correspond to
distinct dynamic market equilibria, or ‘‘regimes’’ which are represented by
different statistical models, for instance, two random walks with different drifts
and (or) variances. The exchange rate series we observe is a mixture of these
regimes, the proportions of which are determined by probabilistic transitions
between states. With a first-order Markov switching process, the probability that a
currency market is in either regime at time t is a function of the regime it was in at
time t 1. One body of research argues that changes in economic fundamentals
render the transition probabilities between two market regimes time varying
(Weinbach, 1993;Diebold, Lee, and Weinbach, l994).
The Markov switching model with time varying transition probabilities is
potentially very useful. It is superior econometrically to the linear regression
models used by Bernhard and Leblang (2002) , Frieden (2002), and other political
scientists because, with the regimes expressed as random walks, the Markov
switching model can account for both the observed nonstationarity in currency
returns and the fat-tailed distributions in these returns (Meese, l990:129–130; see
also Kim and Nelson, 1999). Linear regression models do not account for these
properties. As a result, hypothesis tests based on the estimates from linear
regression models are not as accurate as those based on Markov regime switching
models.10
This is not to say that economists’ applications of the model are adequate for our
purposes. With a few notable exceptions discussed below, the economic research
concentrates on market fundamentals and eschews political analysis. Applications of
the Markov switching model in economics usually make no provision for the
possibility that regime change is precipitated by political factors of any kind. Those
who apply the model in economics do not recognize that the economic
fundamentals on which the transition probabilities depend may be mediated by,
if not the result of, political variables (e.g., Weinbach, l993). The idea that, in young
and incipient democracies, social violence and other kinds of challenges to
government authority might be a source of currency regime switching is nowhere
to be found in the economic literature.
The Markov switching model with time varying probabilities thus offers an
econometrically powerful framework with which to represent the causal mechanism
whereby currency markets respond to politics in young and incipient democracies.
But to realize its potential we need to give it a ‘‘politically relevant’’ interpretation
and translate our theoretical expectations into testable propositions about regime
switching.
10
Space does not permit a methodological critique of existing work on democracy and financial markets. Like
most empirical studies in political science, Frieden’s (2002) measures, model, and diagnostics do not address let
alone capture the three key properties of exchange rates. Hence the hypothesis tests reported in his study are
problematic (e.g., the residuals on which they are based are non-normal). Bernhard and Leblang’s (2002)
investigation addresses the unit root property but not the clustering and fat-tail properties. Suffice it to say that
GARCH models like those used by Leblang and Bernhard (2000a) and Lobo and Tufte (1998) address the clustering
property but do not adequately address the fat tail property of exchange rates. For this reason the distributional
assumptions on which the authors’ hypothesis tests are based also are problematic. The residuals from a Markov
switching model are not plagued by the kurtosis in the same way as those from a linear regression model.
JUDE C. HAYS ET AL.
209
b. A Synthesis
The Markov switching model can be interpreted in the following way. The log
difference in the spot exchange rate follows a random walk because of the way
traders form expectations about the content and consequences of government
policy. The random walk specification implies that the change in a currency’s value
from one period to the next is a random draw from a normal distribution. The
parameters of the random walkFits mean (m) and variance (s2)Freflect both the
prevailing politico-economic informational environment and the optimal decision
rule of the traders; the random walk represents an equilibrium in this sense. But the
informational environment sometimes changes and this alters the optimal trading
rule. Together, these changes produce a regime switch: a change in the parameters of
the random walk (a new equilibrium). A random walk with a relatively high variance
connotes a more frenetic informational environment and a trading rule that is more
sensitive to news. The questions are (1) what kinds of changes to the informational
environment cause currency traders to revise their decision rules so as to produce
random walks with greater variance (volatility)? and (2) what institutions, if any, make
switches to higher variance (more volatile) random walks more and less likely?
The metaphor of a rumor mill is frequently used in the popular press to describe
the type of informational changes that we have in mind. For example, one
commentator from The Economist recently wrote, in a story on Philippine president
Arroyo, that the mill is working at ‘‘double speed’’ grinding out rumors about a
possible coup and the degree of support for former president Estrada among the
masses, military officials, and police corps.11 During times of potentially significant
political change, like the Philippines experienced in early 2002, political speculation
abounds and the probability that traders make decisions on the basis of speculative
rumors increases.12 We contend that it is the propensity of currency traders to
verify these political rumors that distinguishes currency regimes. The tendency to
switch to a decision rule based on unverified political rumors during periods of
political uncertainty is the mechanism whereby politics in young and incipient
democracies spawns exchange rate volatility. 13
New work by Calvo and Mendoza (2000) and others is suggestive here. Calvo and
Mendoza argue that the periods of high volatility in emerging financial markets are
caused by rational contagion, a situation in which traders choose not to pay for
costly information to verify rumors about future returns on investments.14 When
traders base their expectations on unverified rumors rather than fundamentals,
their behavior is more erratic. Consequently, rumor driven investment decisions
produce large capital flows into and out of emerging market economies and this
generates significant volatility in their currency markets. If it is more costly for
11
See ‘‘Happy Anniversary, Mrs. President,’’ The Economist, January 19, 2002, p. 36–37.
The Economist article goes on to note that the government fears the ‘‘talk of political instability is scaring off
investors’’ making it difficult to grow the economy. Of course, coups are not the only type of politics that generates
rumors. During periods of conflict over the future course of economic policy, uncertainty about the government’s
ability to manage its opposition and generate popular support and (or) uncertainty about the government’s survival
prospects more generally, the political rumor mill is likely to be in operation.
13
The ‘‘news’’ approach to modeling exchange rates is popular in financial economics (for a discussion see
Hallwood and MacDonald, 1994:247–252). News, which drives exchange rate dynamics in these models, is
unanticipated changes in fundamentals. In our model, rumors lead to changes in the exchange rate. Rumors are
similar to news in that they are unanticipated. News, however, is always factual, while rumors may not be.
14
Calvo and Mendoza (2000) argue that globalization combined with short selling constraints and performance
based incentives that punish poor portfolio performance more than they reward good performance makes
contagion more likely. That is, globalization, short selling constraints, and performance based incentives increase the
likelihood that it will be an optimal strategy for traders to make changes in their portfolio allocations on the basis of
unverified rumors. Short selling constraints place limits on the ability of traders to profit from knowing the true
value of future returns and performance based incentives make contagion equilibria stable by increasing the cost of
deviating from the herd’s behavior. These incentives make it less likely that the effects of rumors will be undone by
price corrections in the market.
12
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Exchange Rate Volatility and Democratization in Emerging Market Countries
traders to verify political than economic rumors, then rumor driven investment
decisions will likely prevail during periods of political uncertainty when these
rumors are in abundant supply.
Are political rumors more difficult to verify? The small literature in economics
that examines the politics of exchange rates treats political and economic
information in a way that suggests the former is unanticipated or slowly processed by markets. Currencies and other economic variables are treated as
random walks suggesting that currency traders process economic information
quickly and efficiently. In contrast, when they do appear in models, political
variables are represented as dummies or level variables (Bachman, 1992; Blomberg
and Hess, l997; Christodoulakis and Kalyvitis, l997), which suggests that
political information is not anticipated and processed much more slowly
than economic information. Moreover political information arrives in discrete
quantities causing one-shot changes in behavior of currency traders. While
economists rarely offer a rationale for this distinction, we believe it is a reasonable
assumption.
There is a large body of research in political science that suggests it is easier for
traders to forecast market prices than political outcomes. This literature argues that
politics is inherently ill-conditioned and complex and that there are no predictable
political equilibria that are stable over the long term (McKelvey, 1976; Schofield,
1993; Richards, 1994).15 This complexity implies that confirming (or denying) the
validity of political rumors will be problematic and costly, even in countries with
established democratic institutions where political information is readily available.
These problems will be magnified in new democracies and particularly acute in
countries with nondemocratic political institutions and opaque political processes.16
Therefore, we posit that it is more difficult for traders to manage and reduce
political uncertainty than economic uncertainty.
Thus, one can think of the regimes in the Markov switching model as being
driven by separate rumor mills, which specify the politico-economic informational
environment in which traders buy and sell currencies. A schematic of our version of
the model is presented in Figure 2. There are two regimes: a ‘‘fundamentals’’ and a
‘‘contagion’’ regime. The exchange rate follows a random walk in each regime, but
the drift and volatility of the random walk or its mean (m) and variance (s2),
respectively, differ across regimes. The variance is determined by the optimal
trading rule, specifically whether or not traders verify rumors, which is a function
of the informational environment.
The first rumor mill is in operation during periods of political certainty (stability).
It generates rumors that are largely economic in nature and, given the type of
resources available to traders and their economic expertise, relatively cheap to
verify. As a result, traders confirm the veracity of these rumors and only respond to
those that are true. This informational environment generates the ‘‘fundamentals’’
regime in which the observed log difference in the exchange rate has a low
variance. During periods of potential political instability, the second rumor mill
goes into operation. It generates a much higher quantity of political rumors.
Because these rumors are more costly to verify, traders do not check them.
15
Of course, the degree to which a country’s politics is unpredictable may be a function of its institutions.
If, for example, there were a rumor that several pro-reform ministers in a government were going to be
sacked, this might generate pessimistic expectations about the future health of the economy. Regardless of the
transparency of a country’s polity, because of the complexities of the cabinet dissolution and formation processes, it
would be difficult to verify the long-term political and policy consequences of this rumor were it true. But in
countries with more opaque political processes that have limited credible political information available, it would be
either very costly or impossible for traders to determine whether there was or was not any intragovernmental
conflict that threatened the stability of the government in the first place. In a more transparent democracy, a rumor
like this one could be readily disconfirmed by the (free) press, for instance.
16
JUDE C. HAYS ET AL.
211
FIG. 2. Markov Regime Switching Model of Exchange Rates for Emerging Market Economies
Consequently, their buying and selling is more erratic and herd-like. Hence we
observe greater volatility in the exchange rate (s2C4s2F).
The model’s dynamics are represented in Figure 2 with solid and broken line
arrows. The solid line arrows illustrate the evolution of currency markets during
times of political certainty. A market will either remain in or switch to the
‘‘fundamentals’’ regime, and the transition probabilities will reflect this dynamic.
More specifically, pFFt will be close to one and (or) pCC
will be close to zero, which
t
implies a long expected duration in the ‘‘fundamentals’’ regime and (or) a short
expected duration in the ‘‘contagion’’ regime. The broken line arrows show market
dynamics during periods of political uncertainty. A market will either switch to or
remain in a ‘‘contagion’’ regime in these periods. The transition probabilities are
modeled using a logit specification. Proxies for political instability and uncertainty
(denoted in Figure 2 as xt 1)Flike a large drop in presidential approval, a party
defection from the governing coalition, or student riots in the capital cityFshould
help identify which rumor mill is in operation and therefore ‘‘explain’’ the over
time variation in the model’s transition probabilities. In other words, some of the
b’s in the logit specification should be statistically significant and correctly signed.
(For a more technical and complete discussion of the Markov regime switching
model, see Hamilton, 1994:ch. 22; and Diebold et al., 1994.)
When recast in these terms, the political science literature on new and emerging
democracies yields the following propositions:
Because, in the face of political uncertainty, it is not optimal for traders to verify
political rumors, the probability of currency markets remaining in or switching to
high volatility ‘‘contagion’’ regimes increases
PROPOSITION 1: during electoral periodsFespecially for elections in which there is a high
degree of uncertainty about the outcome
212
Exchange Rate Volatility and Democratization in Emerging Market Countries
PROPOSITION 2: the more opinion polls and other sources of political information show
declining support for incumbent executives
PROPOSITION 3: the less stable incumbent coalitions are
PROPOSITION 4: the higher the degree of overt popular opposition to the government and its
policies (e.g., frequency of political riots)
To the extent that they produce less political uncertainty, certain political
institutions will be associated with a higher probability of currency markets
remaining in or switching to a fundamentals regime. In particular,
PROPOSITION 5: Among young democracies, the more consensual the form of government, the
less impact political uncertainty will have on the probability of currency market switching to a
high volatility regime. [Propositions 1–4 will be less likely to hold the more consensual the
young democracy.]
In incipient democracies, where institutions are just taking root, extragovernmental factors will be the primary source of political uncertainty; the markets in
these countries will switch from fundamentals regimes to contagion regimes during
periods of social unrest. Intragovernmental factors like cabinet negotiations will
have comparatively less impact on regime switches:
PROPOSITION 6: Only proposition 4 will hold for emergent democracies.
It is to a test of these propositions that we now turn.
Research Design
We focus on the US dollar exchange rates for Indonesia, the Philippines, South
Korea, and Thailand between March 1998 and September 2000. Thailand, South
Korea, and the Philippines all had democratic institutions in place throughout our
sample period; Indonesia, on the other hand, did not. As noted above, however,
there are important differences between the young democracies. First, they are not
generally viewed as equally democratic. Of the three, Thailand is typically
considered to have the most democratic processes and institutions, followed by
South Korea and the Philippines.17 Additionally, their democracies differ in kind.
The Philippines has a strong presidential political system; South Korea has a
semipresidential system with both a popularly elected president and a prime
minister approved by the parliament; and Thailand has a pure parliamentary
system with electoral institutions that typically produce large multiparty coalitions.
Again, we expect that electoral and, in the cases of South Korea and Thailand,
intra-governmental politics will be important in determining the probability of
currency regime switching in the young democracies (propositions 1–4). The
somewhat consensual nature of Thai political institutions may mitigate this
relationship to some extent (proposition 5). And, because politics is more opaque,
variables like social unrest will have an impact on the probability of currency regime
switches in Indonesia (proposition 6).
Daily exchange rates over the period 3/16/98 – 9/29/00 were used to estimate the
models.18 Over the entire period in which the four countries’ currencies have
floated, there are clearly three currency market regimes: a regime for the initial
crisis period of extreme volatility, a moderately volatile regime, and a low volatility
regime. Because we are estimating a two-regime model, it would be inappropriate
17
The Polity IV scores for Indonesia, the Philippines, South Korea, and Thailand in 1998 are 0, 7, 8, and 9,
respectively, with the low (high) numbers representing the absence (presence) of democratic institutions and
procedures (Polity IV Project, 2000).
18
The exchange rate series are from the PACIFIC Exchange Rate Service’s Historic FX Rates Database.
213
JUDE C. HAYS ET AL.
TABLE 1. Extreme Currency Volatility in the Floating Period
Country
1-Day Currency
Return (US$ Exchange Rate)
Average 1-Day Currency
Return (Absolute Value)
Ratio
Indonesia
1/08/98
1/09/98
1/27/98
2/10/98
2/13/98
30.188
21.122
19.330
23.316
21.677
1.876
1.876
1.876
1.876
1.876
16.091
11.258
10.303
12.428
11.554
Philippines
12/15/97
1/06/98
1/22/01
7.176
6.170
12.518
0.577
0.577
0.577
12.447
10.702
21.713
South Korea
11/20/97
11/21/97
12/08/97
12/09/97
12/11/97
12/15/97
12/16/97
12/22/97
12/23/97
12/24/97
12/29/97
12/30/97
1/30/98
10.105
8.080
8.971
8.714
8.954
8.687
9.340
7.955
13.444
6.888
25.705
14.996
9.700
0.689
0.689
0.689
0.689
0.689
0.689
0.689
0.689
0.689
0.689
0.689
0.689
0.689
14.675
11.735
13.028
12.654
13.003
12.616
13.565
11.553
19.524
10.004
37.330
21.778
14.088
6.574
0.645
10.187
Thailand
3/13/98
NOTE: Dates of extreme volatility are identified by one-day currency returns that are more than ten times the size of
the average one-day currency return during the floating periods for each country.
to use the full sample. We therefore focused on the post-crisis period and the last
two regimes.19 The starting point was determined by comparing the one-day
currency returns with the average daily currency return over the floating period.
Days when the log difference in the exchange rate was more than ten times the size
of the average absolute value of the log difference over this period were excluded
from the sample. These dates, which are listed in Table 1, are all concentrated in the
period before March 16, 1998.20
Several types of political variables were employed in the analysis. First, machinecoded events data from the IDEA project (Bond et al., 1997) were used to construct
measures of social conflict. In particular, these data were used to create three
variables for the degree of social violence and coerciveness: maximum violence,
maximum coercion, and Bond et al.’s conception of conflict carrying capacity
19
The other option was to estimate a three-regime Markov switching model with time varying transition
probabilities. Because of the inherent difficulties in estimating these models and the amount of programming it
would have required to do so, we chose to shorten the sample.
20
Our sample period thus distinguishes our research from that of Haggard and others, which has tended to
focus only on the crisis period. There is, of course, contagion and high volatility in the post-crisis period, but this
volatility is not of the same magnitude as that of the crisis period.
214
Exchange Rate Volatility and Democratization in Emerging Market Countries
(CCC), which measures a regime’s ability to regulate contentious interaction
without violence. Each event that is coded receives a score of 0, 1, 2, 3, or 4, with 0
representing low violence/coercion and 4 representing high violence/coercion. In
the case of violence, a score of 0 is given to events that involve no significant
physical force either threatened or used on another. At the other end of the scale, a
score of 4 is given to events that involve explicit threats or the use of major physical
force. Noncoercive events, given a score of zero, include those that involve no
social, economic, or political costs imposed on another. Highly coercive events,
which receive a score of 4, involve the use of major and explicit costs, either
imposed or threatened on another. Bond et al. (1997) define conflict carrying
capacity as one minus the proportion of events that are violent. Thus, the measure
ranges from 0 to 1.21 Altogether, these variables are indicative of the kind of
political uncertainty that exists about the abilities of governments to manage
opposition and implement their policies.
In addition to these variables we also included election period dummies as well as
some country-specific variables discussed below. As proposition 1 suggests, ceteris
paribus, there will be political uncertainty in the run-ups to elections. Hence traders
are more likely to receive political rumors in these periods.22
Because of the importance of the presidency in Philippine politics, we included
net presidential approval in our analysis of that caseFthat is, the percentage of
people who approve of the president’s performance in office less the percentage
that disapprove. There is substantial movement in this variable over our sample
period. In the early part of 1998, the net approval for Ramos stays at or below 20%
until the election in May won by Estrada. In that month, net presidential approval
jumps to 60% and stays at that approximate level for more than a year. In July of
1999, Estrada’s net approval begins a steady decline, reaching a low in December of
1999 of 5%. Drops in presidential approval should cause political uncertainty and
hence a shift to currency regimes in which political rumors are not verified.23
In South Korea, there are two important executive officers: the president and the
prime minister. The former is popularly elected while the latter is approved by the
parliament. In this semipresidential system, the potential for intragovernmental
conflict is always present. We posit that the stronger the president’s party in the
parliament, the less likely there will be conflict between the president and prime
minister or between the president and the parliament more generally. Under these
conditions, presidents will be better able to appoint prime ministers who share their
political views (and dismiss those who do not). There will be little doubt about the
political influence of the president vis-à-vis parliament. Therefore, we included the
percentage of parliamentary seats held by the president Kim Dae Jung’s party,
which during our sample was the National Congress for New Politics (NCNP), until
January 20, 2000, and the New Millennium Democratic Party (MDP) thereafter.
This seat percentage gradually increases over the course of our sample. When Kim
Dae Jung was inaugurated as president on February 25, 1998, the NCNP held
26.4% of the seats in the National Assembly. By March 30, 1999, as a result of byelections and party defections, the NCNP increased its seat percentage to 35.1%. In
the April 2000 elections, Kim Dae Jung’s party (now the MDP) increased its seat
share to 42.1%. This should have created less political uncertainty and therefore
21
The CCC measure that we use is discussed in detail in Bond et al. (1997:559–562). This measure is refined in
Jenkins and Bond (2001).
22
The dummies are eighty-day periods, forty days up to and including the day of the election and forty days
after.
23
The data is from the Social Weather Station’s special media release (12/22/2000) ‘‘Erap Hangs on to þ 9 netSatisfaction, As Classes and Regions Polarize,’’ which is available online at http://www.sws.org.ph/pr122200.htm. In
our analysis, missing monthly values were filled in using linear interpolation.
JUDE C. HAYS ET AL.
215
lowered the probability that traders would use decision rules that connote the
contagion regime.24
Because coalition politics is a central feature of the Thai system we modeled the
transition probabilities for the baht as a function of the percentage of seats held by
the governing coalition of parties. The Chavalit government whose coalition
members held 65% of the seats in parliament collapsed in November of 1997. It was
replaced with a bare majority six-party coalition (53%) under the leadership of
Chuan Leekpai of the Democratic Party. A little less than a year later, a seventh
party joined the coalition thereby increasing its seat share in the parliament to 66%.
The Social Action Party withdrew from the coalition in July of 1999 dropping the
coalition’s percentage of seats in the parliament to 61%. This may have created
enough uncertainty to increase the probability of traders switching to the contagion
regime. But, once more, Thailand’s somewhat consensual institutions may be a
mitigating factor.
Results
The switching models were constructed in a couple of steps. First, fixed transition
probability models were estimated using maximum likelihood. Then several
pretests were performed to establish the model structure for each country. The
pretests included Garcia’s test for one versus two currency regimes, Wald tests for
the equality of means and variances across regimes as well as simple versus Markov
switching, and finally the D’Agostino z-test for excess kurtosis. 25 Next, the time
varying transition probability models were estimated using our political variables.
Because of the nonlinear nature of the Markov regime-switching model, it is often
difficult to converge on a solution with more than one variable in the transition
probability vector. Therefore, we started by estimating our time-varying transition
probability models with one political variable at a time. If we found evidence that
more than one variable was statistically significant, we attempted to reestimate the
model with all the statistically significant variables included in the transition
probabilities simultaneously.26
If the propositions hold, our results should indicate when political uncertainty
causes traders to alter their decision rules and choose not to verify political rumors,
shifting the market to a high volatility currency regime. Table 2 contains the results
for Indonesia. The constant transition probability estimates and pretests are
reported in the first column. The pretests indicate that there are two currency
regimes. The Garcia test statistic is statistically significant. Neither drift parameter is
statistically significant so the two regimes are distinguished by their degree of
volatility. In fact, the second regime is more than twelve times as volatile as the first.
The test for simple switching (H0: pCC ¼ 1 – pFF) is rejected. Currency rates in
24
Some observers interpreted the 2000 elections as a setback for Kim Dae Jung because the opposition Grand
National Party (GNP) gained seats at the expense of the UDL, the NCNP/MDP’s coalition partner. For an alternative
interpretation see Haggard (2000:107).
25
Simple switching models are those in which the probability of being in a particular state is the same regardless
of the previous state. Garcia (1998) provides a method for testing the null hypothesis of a single regime by deriving
the nonstandard asymptotic null distribution of the LR statistic to obtain critical values. The z-test for excess kurtosis
is described in D’Agostino et al. (1990). Following Kim and Nelson (1999), these tests were performed on the
standardized residuals from the model.
26
There is some research that suggests good and bad news (i.e., unanticipated changes in economic variables)
have asymmetrical effects on currency market volatility. For example, Leblang and Bernhard (2000a) argue that
unanticipated negative shocks may produce larger increases in volatility than unanticipated positive shocks. If we
find that political uncertainty shifts currency markets into higher volatility regimes, then our results are entirely
consistent with the spirit of this argument: political uncertainty increases currency market volatility by much more
than political certainty. Additionally, as long as there is persistence in the two regimes (b0 4 0) the nonlinear
functional form of our transition probabilities guarantees that political certainty and uncertainty will have the
anticipated asymmetrical effects on currency market volatility.
0.352 [.839]
1370.514
Parliamentary Elections, 6/7/99.
nn
6.564 [.038]
1367.408
NOTES: Standard errors in parentheses. Brackets contain p-values. np-value o.01
302.055n
10.701 [.000]
3.241 [.196]
Kurtosis
Currency Returns
Stan. Residuals
Likelihood Ratio Tests
Garcia
Constant vs. Time-Varying
164.39 [.000]
0.421 [.516]
20.069 [.000]
Wald Tests
H0: pCC ¼ 1 pFF
H0: mF ¼ mC
H0: s2F ¼ s2C
1370.690
1.372 (1.122)
0.050 (.425)
1.530 (1.282)
0.881 (.418)
1.470 (.310)
bC0
bC1
log(L):
2.905 (1.161)
0.125 (.300)
2.858 (2.913)
0.106 (.742)
2.500 (.315)
bF0
bF1
1.301 (.243)
16.528 (3.593)
1.279 (.257)
16.187 (3.359)
1.292 (.232)
16.563 (3.550)
0.046 (.112)
0.188 (.535)
Maximum Daily
Violence Score
s2F
s2C
0.053 (.092)
0.199 (1.027)
Maximum Daily
Coercion Score
0.044 (.066)
0.183 (.340)
Constant Transition
Probabilities
mF
mC
Parameter
0.790 [.674]
1370.295
1.264 [.532]
1370.058
1.549 (.353)
0.900 (1.10)
2.462 (.318)
0.100 (1.10)
1.330 (5.739)
4.199 (6.350)
0.472 (1.441)
1.114 (2.360)
1.274 (.249)
16.396 (3.623)
0.046 (.096)
0.184 (.365)
Electionnn
Period Dummy
1.303 (.225)
16.596 (3.446)
0.046 (.064)
0.188 (.413)
CCC
TABLE 2. Markov Switching Models for the Indonesian Rupiah (US$ Exchange Rates, 3/16/98–9/29/00)
216
Exchange Rate Volatility and Democratization in Emerging Market Countries
JUDE C. HAYS ET AL.
217
Indonesia are governed by a Markov switching process where the probability of
being in each regime depends on the previous state of the market. Finally, the
model removes all of the excess kurtosis from the unconditional distribution of
currency returns. A normal distribution has a kurtosis of three. The raw
distribution of rupiah-dollar currency returns for our sample had a kurtosis of
10.701. After estimating the model, however, we are unable to reject the hypothesis
that its standardized residuals have a kurtosis of three.
Columns 2–6 of Table 2 report the estimated impact of our political variables on
the transition probabilities for the rupiah-dollar currency market. The coefficients
bF0 and bC0 are the constants in the logistic expression for pFF and pCC, respectively,
while bF1 and bC1 are coefficients for the political variable (xt 1) listed at the top of
each column (cf. Figure 2). Indonesian politics clearly affects the behavior of
currency traders. Individual coefficient and likelihood ratio tests for the maximum
daily coercion score are statistically significant. Moreover, the sign of the coefficient
is consistent with our expectations. The exchange rate and coercion series are
plotted in Figure 3a. It shows that on days when there are highly coercive events,
the market is more likely to persist in the high volatility regime. For example, when
the maximum daily coercion score is 2, the probability of staying in high volatility
currency regime is .56, giving an expected duration in this regime of a little over
two days.27 When the maximum daily coercion score is increased to 4, the
probability of staying in the high volatility regime increases to .88 and the expected
duration rises to over eight days (see Figure 3b). No such results are found for the
maximum daily violence or for Bond et al.’s CCC measure. Nor did the Indonesian
election on June 7, 1999 have an impact on currency trading. Rather, in this case, it
is social coercion that causes the change in traders’ decision rules.
The results for the Philippines are contained in Table 3. Again, there are two
regimes distinguished by their volatility, the estimated variance in the second
regime being almost sixteen times as great as the variance in the first regime. The
model removes most but not all of the excess kurtosis in the currency returns. The
kurtosis goes from 13.093 in the unconditional distribution to 4.296 in the
standardized residuals. Individual coefficient and likelihood ratio tests show that
domestic politics has an impact on regime switching. The election period dummy is
statistically significant. During election periods, in the Philippines, the market is
more likely to switch out of the low volatility regime. The coefficient on Bond et al.’s
conflict carrying capacity variable is positive and also statistically significant in the
low volatility regime. (The exchange rate series and conflict carrying capacity are
plotted in Figure 4a.) As conflict carrying capacity rises, the market is more likely to
persist in this regime. For example, when CCC is .85, the expected duration in the
low volatility depreciation regime is a little over 3 weeks. When CCC rises to 1, the
expected duration increases to a little less than three months (see Figure 4b).
Interestingly, government coercion, maximum daily violence, and presidential
approval do not affect the dollar-peso market. Since both the election dummy and
the CCC variable are statistically significant, we finish by estimating a model with
both of these variables in the transition probabilities. When we do this, both
variables remain statistically significant and correctly signed in the ‘‘fundamentals’’
regime. This final model clearly outperforms the others in terms of identifying the
currency regime in our sample period.
Table 4 provides the results for South Korea. Again, the Garcia test indicates that
there are two regimes. Since neither drift parameter is statistically significant, the
regimes are distinguished by their volatility with the variance in the second regime
more than 17 times that in the first. Again, the model removes most but not all of
the excess kurtosis initially displayed in the won-dollar currency returns. The
27
The expected duration in a regime is 1/(1 pii), where i ¼ F,C.
1.138 [.566]
0.063 (.010)
0.989 (.179)
0.035 (.013)
0.007 (.323)
Maximum
Violence
Score
2.483 (.511)
0.600 (.900)
3.608 (5.970)
7.112 (6.500)
5.014 [.082]
12.946 [.002]
412.178
3.562 (.358)
5.100 (1.600)
4.193 (2.961)
8.650 (3.500)
416.144
0.063 (.008)
1.045 (.206)
0.035 (.013)
0.006 (.028)
Election
Period
Dummynn
0.062 (.007)
0.978 (.164)
0.033 (.013)
0.010 (0.052)
CCC
Presidential Election, 5/11/98.
nn
1.806 [.405]
417.748
4.334 (6.227)
0.525 (1.96)
4.367 (.947)
0.315 (.385)
NOTES: Standard errors in parentheses. Brackets contain p-values. np-value o.01
447.560n
13.093 [.000]
4.296 [.000]
Kurtosis
Currency Returns
Stan. Residuals
Likelihood Ratio Tests
Garcia
Constant vs. Time-Varying
1008.6 [.000]
0.262 [.609]
28.483 [.000]
Wald Tests
H0: pCC ¼ 1 pFF
H0: mF ¼ mC
H0: s2F ¼ s2C
418.082
1.648 (1.264)
0.525 (.525)
3.052 (.544)
bC0
bC1
bC2
418.651
3.497 (2.254)
0.025 (.800)
3.577 (.368)
bF0
bF1
bF2
log(L):
0.063 (.008)
0.995 (.178)
0.062 (.008)
0.978 (.174)
s2F
s2C
0.035 (.013)
0.007 (.543)
Maximum
Coercion
Score
0.034 (.013)
0.008 (.048)
Constant
Transition
Probabilities
mF
mC
Parameter
1.672 [.433]
417.815
2.356 (.751)
0.014 (.022)
4.005 (.549)
0.011 (.015)
0.062 (.008)
0.982 (.182)
0.034 (.013)
0.008 (.051)
Net
Presidential
Approval
TABLE 3. Markov Switching Models for the Philippine Peso (US$ Exchange Rates, 3/16/98–9/29/00)
17.840 [.001]
409.731
3.170 (9.972)
6.099 (10.905)
0.142 (2.265)
4.295 (3.138)
8.759 (3.681)
5.153 (1.888)
0.063 (.008)
1.022 (.196)
0.033 (.014)
0.009 (.046)
CCC / Election
Period Dummynn
218
Exchange Rate Volatility and Democratization in Emerging Market Countries
JUDE C. HAYS ET AL.
219
FIG. 3.(a) Indonesia, Exchange Rate, and the Maximum Daily Coercion Score, 3/16/98–9/27/00 (b)
Maximum Daily Coercion Scores and Regime Switching in the Rupiah/Dollar Exchange Rate
kurtosis drops from 19.556 to 5.051. The estimated coefficients on the maximum
daily violence score and the NCNP/MDP parliamentary seat percentages are both
statistically significant and the estimates have the expected sign. The higher the
maximum daily violence score the more likely the market is to switch out of the low
volatility currency regime. The higher the parliamentary seat percentage of the
president’s party the longer the market will persist in the low volatility currency
regime: the expected duration in the low volatility regime increases from 2.7 days
to more than 70 days as the seat percentage of the NCNP/MDP increases from 25 to
40 (see Figures 5a and 5b). Neither social coerciveness nor Bond et al.’s CCC
variable has any impact on regime switching in the Korean case. Since both the
220
Exchange Rate Volatility and Democratization in Emerging Market Countries
FIG. 4.(a)Philippines, Exchange Rate, and Conflict Carrying Capacity, 3/16/98–9/27/00 (b) Conflict
Carrying Capacity and Regime Switching in the Peso/Dollar Exchange Rate
maximum daily violence and the parliamentary seat percentage variables are
statistically significant, we estimate a model with both in the transition probabilities.
This time only the seat percentage variable remains statistically significant. Once we
control for this variable, maximum daily violence does not improve our ability to
identify the currency regimes, which suggests social violence does not generate the
kind of political uncertainty in South Korea that causes traders to alter their
decision rules.
The results for Thailand are presented in Table 5. As in all of the previous cases,
there are two regimes. The Garcia test statistic is significant at the .01 level. Neither
(.016)
(.080)
(.020)
(.426)
(.382)
432.899n
19.556 [.000]
5.051 [.000]
111.19 [.000]
0.580 [.446]
7.648 [.006]
471.128
2.218 (.759)
0.018
0.083
0.070
1.207
2.849
Constant
Transition Probabilities
(.017)
(.108)
(.025)
(.625)
(8.898)
(2.600)
(.016)
(.089)
(.029)
(.571)
(5.359)
(1.500)
5.342 [.069]
468.457
1.941 (1.494)
0.007 (.700)
0.019
0.087
0.076
1.313
6.823
1.292
Maximum
Violence Score
(.018)
(.082)
(.020)
(.639)
(57.366)
(58.80)
58.496 (286.17)
58.555 (294.00)
0.018
0.078
0.068
1.147
16.840
14.570
CCCnn
1.318 [.517]
470.469
Three-period moving average.
nn
0.258 [.879]
470.999
3.545 (6.001)
0.400 (1.900)
0.017
0.086
0.072
1.241
3.098
0.100
Maximum
Coercion Score
NOTES: Standard errors in parentheses. Brackets contain p-values. np-valueo.01
Wald Tests
H0: pCC ¼ 1 pFF
H0: mF ¼ mC
H0: s2F ¼ s2C
Kurtosis
Currency Returns
Stan. Residuals
Likelihood Ratio Tests
Garcia
Constant vs. Time-Varying
mF
mC
s2F
s2C
bF0
bF1
bF2
bC0
bC1
bC2
log(L):
Parameter
(.015)
(.101)
(.031)
(.647)
(2.607)
(.092)
25.236 [.000]
458.510
4.783 (2.672)
0.117 (.096)
0.018
0.099
0.081
1.465
5.594
0.246
Parliamentary Seat
Percentage (NCNP/MDP)
TABLE 4. Markov Switching Models for the South Korean Won (US$ Exchange Rates, 3/16/98–9/29/00)
(.017)
(.105)
(.033)
(.673)
(3.708)
(.389)
(.105)
(2.706)
(.466)
(.102)
27.066 [.000]
0.019
0.098
0.081
1.476
4.209
0.336
0.231
4.764
0.052
0.121
457.595
Max Violence /
Seat Percentage
JUDE C. HAYS ET AL.
221
222
Exchange Rate Volatility and Democratization in Emerging Market Countries
FIG. 5.(a) South Korea, Exchange Rate, and Parliamentary Seat Percentage of NCNP/MDP, 3/16/98–9/
27/00 (b). Seat Percentage of President’s Party and Regime Switching in the Won/Dollar Exchange
Rate
drift parameter is statistically significant. The currency regimes, therefore, are
distinguished by their volatility with the variance in regime two being eleven times
as great as the volatility in regime one. The regime-switching model removes
enough of the excess kurtosis from the initial distribution of currency returns that
we are unable to reject the null hypothesis of residual kurtosis equal to three at the
.05 level of statistical significance. In the Thai case, there is no evidence that
extragovernmental variables cause shifts in traders’ decision rules. But there is
evidence that intragovernmental variables cause these shifts. The coefficient on the
coalition seat percentage variable is statistically significant in the low volatility
nn
0.070 [.791]
NOTES: Standard errors in parentheses. Brackets contain p-values. np-value o.01
Wald Tests
60.835 [.000]
H0: pCC ¼ 1 pFF
H0: mF ¼ mC
0.074 [.785]
H0: s2F ¼ s2C
1.088 [.297]
Kurtosis
Currency Returns
12.728 [.000]
Stan. Residuals
3.399 [.053]
Likelihood Ratio Tests
Garcia
30.034*
Constant vs. Time-Varying
524.883
0.216 [.642]
524.810
1.701 (2.457)
0.150 (.058)
3.300 (2.676)
0.135 (.180)
1.486 (2.153)
0.019 (.022)
0.021 (.338)
Maximum Violence Score
Three- period moving average.
1.278 (1.263)
0.225 (.425)
2.031 (.614)
bC0
bC1
524.918
3.393 (1.496)
3.259 (1.864)
bF0
bF1
log(L):
0.141 (.080)
1.562 (1.047)
0.019 (.019)
0.024 (.134)
Maximum Coercion Score
0.136 (.118)
1.491 (1.414)
0.019 (.018)
0.022 (.146)
Constant Transition Probabilities
s2F
s2C
mF
mC
Parameter
0.846 [.358]
524.495
2.179 (.740)
39.930 (45.830)
37.311 (46.656)
0.144 (.026)
1.787 (.573)
0.016 (.034)
0.022 (.997)
CCCnn
TABLE 5. Markov Switching Models for the Thai Baht (US$ Exchange Rates, 3/16/98–9/29/00)
22.130 [.000]
513.853
5.362 (7.015)
0.116 (.124)
21.040 (8.773)
0.418 (.161)
0.162 (.017)
2.143 (.543)
0.020 (.019)
0.055 (.151)
Coalition Seat Percentage
JUDE C. HAYS ET AL.
223
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Exchange Rate Volatility and Democratization in Emerging Market Countries
FIG. 6.(a) Thailand, Exchange Rate, and Seat Percentage of the Governing Coalition, 3/16/98–9/27/00
(b). Seat Percentage of Governing Coalition and Regime Switching in the Baht/Dollar Exchange Rate
currency regime and has the anticipated sign. Because of the fluidity of coalitional
politics in Thailand, this variable may serve as a good proxy for the probability of
cabinet dissolution. The withdrawal of the Social Action Party from the government
coalition in July of 1999 is an example of this fluidity. Because the government had
a supermajority in this period, the defection was largely inconsequential. However,
had the SAP withdrawn during the first Chuan government, the impact would have
been much more significant: the coalition would have lost its majority status in the
parliament.
Our results suggest that as the governing coalition’s seat percentage drops, the
market is more likely to switch out of the low volatility regime. For example, when
JUDE C. HAYS ET AL.
225
the percentage of parliamentary seats held by the governing coalition is 65, the
average of the two periods of supermajority coalitions, the market is essentially
locked into the low volatility currency regime with an expected duration of close to
460 days. When the percentage of seats drops to 53, which is the percentage of
parliamentary seats held by the first Chuan government from November 1997 to
November 1998, the expected duration in the low volatility currency regime falls to
just 4 days (see Figures 6a and 6b). It should be noted that the likelihood ratio
statistic for this model is highly significant (p-valueo.001).
Discussion
Overall, our results provide support for propositions 1, 3, 4, and 6. With regard to
proposition 1, we found that during the period surrounding the Philippine
presidential election, the peso-dollar currency market was more likely to switch out
of the low volatility currency regime. Given that this election was ‘‘strongly
contested but highly uncertain’’ (Haggard, 2000:130), this result is consistent with
the idea that electoral politics causes currency traders to adopt decision rules
whereby they do not verify political rumors (proposition 1). We also found that
during the periods when the Thai coalition government’s future would have been
most uncertainFthat is, periods when the government had a minimal share of the
legislative seats and therefore was most vulnerable to defectionFthe baht-dollar
market was less likely to persist in the low volatility currency regime; this evidence
supports proposition 3 but refutes proposition 5. Proposition 5 was derived from
previous research that focused on the advanced industrial democracies, specifically
Germany (Freeman et al., 2000). The different impacts that coalition politics has on
the Thai and German currency markets may reflect differences between young and
long-standing democracies. Or it may be attributable to differences in their
respective party systems, like the presence of strong parties and government
coalitions that are dimension-by-dimension medians with empty win sets in the
latter country but not in the former (Laver and Shepsle, 1996; MacIntyre,
2001:118).
In the cases of Indonesia and the Philippines we found support for proposition 4:
during periods of high social violence and/or coercion, traders use decision rules
whereby they do not verify political rumors, hence currency markets are more
likely to remain in or switch to high volatility regimes. The fact that our social
conflict variables did not affect regime switching in the baht-dollar and won-dollar
markets most likely reflects the degree of democratic development in Thailand and
South Korea. Conversely, in the incipient democracy Indonesia, we found that
maximum daily coercion and not the more familiar political variable, in this case an
election dummy, had a statistically significant impact on currency market switching.
This result is consistent with proposition 6. Finally, our results provide mixed
support for proposition 2. Net presidential approval did not have a statistically
significant impact on regime switching in the peso-dollar market. But changes in
the legislative seat percentage of Kim Dae Jung’s partyFwhich rose over the
period of our sample partly as a result of his popularityFdid have a statistically
significant impact on currency regime switching in the won-dollar market.
Conclusion
Our research both reinforces and develops the existing arguments about
democratization and globalization. We agree with, and our results support,
Haggard’s (2000) conclusions about the linkages between transparency, political
certainty, and financial crises. Because politics generates substantial uncertainty in
countries with less democratic institutions and more opaque policy processes, these
countries suffer more from volatility in international financial markets. In fact, our
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Exchange Rate Volatility and Democratization in Emerging Market Countries
estimate of the variance of Indonesia’s high currency volatility regime was more
than ten times larger than the estimated variances for the other countries. In this
sense, then, globalization and democratization in emerging market countries are
compatible processes.
At the same time, however, democracy does not eliminate political uncertainty
completely. And because it is difficult to verify the political rumors that circulate
during times of uncertainty, even in young democracies with consolidated
institutions and processes, there is a possibility that the globalization of financial
markets will impose some costs. Our empirical results suggest this is true. Some
form of routine democratic politics affected currency market volatility in all of the
young democracies that we studied.
In this respect, our analysis is consistent with MacIntyre’s (2001) argument that
most developing democracies are more susceptible to financial crises because they
have either widely dispersed or extremely concentrated veto authority as opposed
to the developed democracies which have distributions of veto authority falling
somewhere between the two extremes. Similarly, our empirical results lead us to
conclude that most developing democracies are susceptible to currency volatility
and that there are significant differences between the mature and young
democracies in this regard (cf. Freeman et al., 2000). Most important, because
the nature of coalition politics seems to be more fluid in developing countries, PR
electoral systems do not necessarily stabilize democratic politics in a way that
reduces the uncertainty elections and cabinet dissolutions generate; consequently
PR systems do not lessen the potential for democratic politics to create currency
market volatility in developing democracies.
MacIntyre’s framework is illuminating. But it is also too simple. Our research
shows the value from adding political context to his veto points logic. MacIntyre may
be right that the presidential system in the Philippines allows a mix of policy
flexibility and restraint that, most of the time, is reassuring to international investors.
However, we found that presidential elections create significant currency volatility,
which suggests that because of the importance of the presidency in this system these
elections can make traders very nervous. Our analysis of the Thai case highlights the
important role that the size of governing coalitions plays in determining the amount
of political uncertainty generated by coalition politics. The veto power of small
parities in governing coalitions (a very important source of political uncertainty) is
maximized when governments are bare majorities rather than supermajorities.
Finally, the South Korean case demonstrates the importance of political context in
semipresidential systems. Simply put, intragovernmental conflict, a source of political
uncertainty and hence currency market switching, is more likely in these systems
when the president’s party is underrepresented in the legislature because parliament
is more likely to challenge the president under these conditions.
To conclude, we note that, by focusing on the post-crisis period, we have expanded
the scope of research on domestic politics in emerging market countries and the
behavior of international financial markets. Currency traders monitor and react to
politics continuously. Therefore, research that goes beyond the politics of financial
meltdowns, speculative currency attacks, and other ‘‘rare events’’ is clearly needed
(Haggard, 2000; Leblang, 2001; MacIntyre, 2001). We addressed this need by
exploring the politics of currency market volatility. While periods of volatility have
serious welfare consequences, they are, at the same time, much more frequent and
‘‘normal’’ than the events that consume much of the existing scholarly research.
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