WPS 2 132
POLICY RESEARCH WORKING PAPER 2132
A Regime-Switching A regime-switching
framework is used to study
Approach to Studying speculative attacks against
Speculative Attacks European Monetary System
currencies during !979-93
A Focus on European Monetary
System Crises
Maria Soledad Marttnez Peria
The World Bank
Development Research Group
Finance
June 1999
I POIICY RESEARCH WORKING PAPFR 2132
Summary findings
Peria uses a regime-switching trarnework to study Z Both economic fundamentals and expectations
speculative attacks against European Monetary System determine the likelihood of switching from a period of
(EMS) currencies during 1979-93. tranquility to a speculative attack. The budget deficit
She icdentifies speculative attacks by modeling appears to be an especially important factor driving the
exchange rates, reserves, and interest rates as time series probability of switching to a speculative regime.
subject t-o discrete regime shifts. She assumes two states: Given the importance of anticipating and, wherever
"tranquil" and "speculative." possible, avoiding crises, it might be useful to conduct
She models the probabilities of switching between forecasting exercises to determine whether the switchinig
states as a function of fundamentals and expectations. framework proposed here can be used to forecast crises
She concludes that: in countries outside the sample.
* The switching models with time-varying transition Because currency crises tend to occur simultaneously
probabilities capture most of the conventional episodes in two or more countrres, it also might be useful to adapt
of speculative attacks. the regime-switching framework to explore the role of
* Speculative attacks do not always coincide with contagion in explaining crises.
currency realignments.
This paper - a product of Finiance, Development Research Group - is part of a larger effort in the group to understand
currency crises. Copies of the paper are available free fron the World Bank, 1818 H Street NW, Washington, DC 204.33.
Please contact Agnes Yaptenco, room MC3-446, telephone 202-473-8526, fax 202-522-1155, Internet address
ayaptenco@Tworldbank.org. Policy Research Working Papers are also posted on the Web at http:/!/vww7\w.worldbank.org/
html/dec/Publications,'Workpapers,;home.html. The author may be contacted at mmarrinezperia yworldbank.org. June
1999. (52 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about 1
development issoes. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papess caury the namnes of the authors and should be cited accordingly. The bo:dicigs, interpretations, anid conclusions expressed in this
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Produced by the Policy Research Dissemination Center
A Regime Switching Approach to Studying Speculative Attacks:
A Focus on European Monetary System Crises
Maria Soledad Martinez Peria
Development Research Group
World Bank
JEL Classification Codes: F30, F33, C32
Keywords: Exchange rates, speculative attacks, EMS, Markov regime-switching models
I am especially indebted to Barry Eichengreen, Richard Lyons, Maurice Obstfeld, and James Powell for their
advice and encouragement. I greatly benefited from discussions with David Bowman, Jon Faust, Michael Gibson,
Dale Henderson, Chang-Tai Hsieh, Steve Kamin, Andy Levin, Gian Maria Milesi-Ferreti, John Rogers, Tom
Rothenberg, Paul Ruud, and Sergio Schmukler. Furthermore, fruitful suggestions were provided by seminar
participants at related presentations at the Central Bank of Argentina, Dartmouth College, the Federal Reserve Bank
of Boston, the Federal Reserve Board, the Inter-American Development Bank Tufts University, Universidad San
Andres, Wellesley College, Wesleyan University, and the World Bank Research Department Finally, I would like
to express my gratitude to Matthew Jones and Gretchen Weinbach for their help with the empirical estimations of
the EM algorithm.
Contact information: Maria Soledad Martinez Peria, World Bank, 1818 H Street, NW, Room MC3-455,
Washington, DC 20433. Phone: (202) 458-7341. Fax: (202) 522-1155. E-mail: mnrartinezperiagworldbank.org.
1- Introduction
In recent years, both developed and developing countries have experienced speculative
attacks on their currencies. The European Monetary System (EMS) was severely undermined by
intense speculative pressure in 1992-93, which led to the exit of Britain and Italy in 1992 and the
widening of the Exchange Rate Mechanism (ERM) band in 1993. In December 1994, following
a speculative attack on the Mexican peso, the crawling peg was abandoned and the currency
devalued. More recently, turbulence in financial markets has crippled many Asian currencies as
well as the Russian ruble.
This recent wave of speculative attacks and devaluations has rekindled interest in the
subject. The conventional wisdom is that speculative attacks refer to episodes where currencies
come under severe pressure to be devalued. These episodes may or may not result in
devaluations. For example, governments faced with speculative pressure can try to avoid
devaluations by selling reserves and/or by raising interest rates.
Most papers in the literature of speculative attacks pursue a two step approach to identify
and to study the determinants of speculative attacks (see Eichengreen, Rose, and Wyplosz (1995,
1996), Frankel and Rose (1996), and Kaminsky and Reinhart (1996) among others). First, they
identify speculative attacks by constructing indices of speculative pressure. These are weighted
averages of changes in exchanges rates, interest rates, and reserves. A given episode is classified
as a speculative attack if the index is above a certain threshold. Based on this classification, a
zero/one variable is constructed, which identifies the speculative attack episodes in the sample.
Second, an analysis of the determinants of the likelihood of a speculative attack is conducted by
estimating logit or probit models where the zero/one variable discussed above is used as the
dependent variable.
1
This paper proposes a different approach to study speculative attacks. Fixed exchange
rate regimes are typically characterized by periods of relative calm punctuated by short and sharp
periods of speculative attacks. Given the nature of fixed exchange rate systems, this paper
implements a regime-switching model with time-varying transition probabilities to,
simultaneously, identify speculative attacks and study the determinants of switching to
speculative regimes.
Though regime shifts are not directly observable, we can draw probabilistic inferences on
these episodes from the behavior of observable variables. In particular, speculative attacks are
characterized by: sharp falls in reserves, depreciations of the exchange rate, and/or increases in
interest rates.' Thus, this paper identifies speculative attacks by modeling reserves, exchange
rates, and interest rate differentials as time series subject to discrete shifts in regimes. In other
words, we assume the behavior of these variables is different depending on the state in which the
economy resides. We assume a "tranquil" state where the variables above are stable, and a
"speculative" regime characterized by large depreciations of exchange rates, reserve falls,
interest rate hikes, and/or an overall increase in the volatility of these variables.
The approach pursued in this paper is in the spirit of Hamilton's (1990) regime-switching
model where a vector of parameters is allowed to switch, potentially every period, depending on
which of two unobserved states is realized. In Hamilton's framework, state transitions are
governed by a first-order Markov process and the probabilities assigned to switching or staying
in a given state (called transition probabilities) are assumed to be constant. However, because the
assumption of constant transition probabilities is very restrictive, and because we would like to
know what drives these probabilities, this paper adopts the algorithm proposed by Diebold, Lee,
When a currency comes under attack, the government can allow the currency to depreciate, lose reserves while
2
and Weinbach (1994). This algorithm allows transition probabilities to be time-varying functions
of observable variables.
Following the literature on speculative attacks and devaluations, we model transition
2
probabilities as logistic functions of economic fundamentals a la Krugman (1979). Also, we
include expectations' proxies such as interest rate differentials and survey data on expected
exchange rates to allow for the possibility that expectations drive speculative attacks.
The regime-switching approach (with time-varying transition probabilities) proposed here
to study speculative attacks has two main advantages. First, this method of identifying attacks
avoids the arbitrariness involved in the construction of indices of speculative pressure. To
construct an index of speculative pressure it is necessary to determine which variables to include
and what weights should be given to each variable. Instead, in the regime-switching approach,
the parameters estimated in the model and the data reveal the state (tranquil or speculative) in
which the economy resides at each point in time. The second main advantage of the regime-
switching approach is that it can directly address the question of what causes shifts from and to
periods of speculative attacks. In particular, by modeling transition probabilities as a function of
fundamentals and expectations, we are able to study the determinants of speculative attacks.
This paper implements the regime-switching approach to study speculative attacks on
EMS currencies during the period 1979-1993. We estimate two switching models. In the first
case, changes in the exchange rate alone are associated with the unobserved regimes. Thus, in
this first model, speculative attacks manifest as realignments or as significant depreciations of
the exchange rate. The second model is a VAR switching model of changes in exchange rates,
keeping the exchange rate fixed, and/or raise interest rates to deter speculation.
2 These fundamentals include: the growth of domestic credit, the ratio of imports to exports, the real exchange rate,
the unemployment rate, and the fiscal deficit.
3
reserves, and interest rate differentials. In this model, all three variables are affected by the state
(speculative or tranquil) in which the economy resides at any point in time. In both models, we
allow transition probabilities to be a function of fundamentals and expectations.
The rest of the paper is organized as follows. Section 2 reviews the theoretical and
empirical literature on speculative attacks. Section 3 briefly summarizes previous empirical
studies on exchange rates that estimated switching models. Section 4 discusses the empirical
approach pursued in this paper. Section 5 describes the data used in this study. Section 6 presents
the empirical results obtained. Section 7 examines the question of how well the switching
approach captures attacks. Finally, section 8 concludes.
2- The literature on speculative attacks
Two types of models, the so-called "first generation" and "second generation" models,
(lominate the existing literature on the determinants of speculative attacks and devaluations. The
first generation models (see Krugman (1979) and Flood and Garber (1984a) among others)
emphasize the relationship between speculative attacks and economic fundamentals. According
to these models, countries suffer attacks when they run unsustainable monetary and fiscal
policies.
In Krugman's (1979) seminal article the balance of payments crisis is driven by an
exogenous budget deficit financed by monetary expansion. The model developed by Krugman
has been extended in various ways. Relaxing his assumption of purchasing power parity for
example, shifts to expansionary policies increase the demand for domestic goods, causing their
price to rise and leading to real exchange rate appreciations prior to attacks. In turn, the trade
deficit is likely to grow before attacks due to the appreciation in the real exchange rate.
4
Recent theoretical research on speculative attacks has deviated from the external balance
fundamentals to focus on how internal balance factors can cause speculative attacks. For
example, Ozkan and Sutherland (1994) expand the set of relevant fundamentals associated with
attacks to include internal balance factors like the unemployment rate. In their model, there may
be no evidence of monetary or fiscal imbalances prior to the attack. However, the authors assume
that the policies implemented by the government, which are consistent with the exchange rate
peg, increase unemployment. They argue that if the government's survival probability falls as
unemployment rises, and if switching to more expansionary policies reduces unemployment, the
government may be forced to abandon the peg.3
The second generation models (see Obstfeld (1986, 1994), Flood and Garber (1984b)
among others) allow speculative attacks to be self-fulfilling. Thus, in these models expectations
play a crucial role in bringing about speculative attacks. If no speculative attack takes place,
government policies are consistent with the exchange rate peg and the peg is maintained.
However, if and only if an attack occurs, government policies become more accommodating,
causing the exchange rate to depreciate. Thus, given the policies pursued by the authorities
following the attack, the attack is rational.
The empirical literature on currency crises has mostly focused on the study of discrete
devaluations in developing economies.4 Blanco and Garber (1986), Cumby and Van Wijnbergen
(1989), and Goldberg (1994) estimate one-period ahead probabilities of devaluations derived
from structural models in the flavor of Krugman's model. These papers study devaluations in
Argentina (in 1981) and Mexico (in 1982 and in 1986), respectively. Edwards (1989), Klein and
3To the extent that the rise in unemployment is due to the government's effort to defend from a speculative attack
by rising interest rates, this model can also be considered as an example of self-fulfilling crises.
4For a comprehensive review of the empirical literature on speculative attacks see Kaminsky, Lizondo, and Reinhart
(1997).
5
Marion (1997), and Martinez Peria (1997) use probit and logit models to estimate likelihoods of
devaluations as a function of economic fundamentals in developing countries, in the first case,
and Latin American economies, in the last two cases. Though the studies mentioned above are
valuable, they have a number of limitations. They only consider devaluations as opposed to
speculative attacks in general.5 They do not allow expectations to play a role in explaining
speculative attacks. Finally, they restrict their analysis of the determinants of currency
devaluations to the case of developing countries.
While the studies above have attempted to characterize devaluations exclusively, fewer
studies have analyzed a broader class of crises, namely speculative attacks where devaluations
may be averted at the expense of sizeable losses in reserves and/or large increases in interest
rates. Eichengreen, Rose, and Wyplosz (1995, 1996), ERW hereafter, Kaminsky and Reinhart
(1996), and Frankel and Rose (1996) are the main contributors to this literature. These studies
construct indices of speculative pressure to identify attacks against developed and developing
countries' currencies. These indices are weighted averages of reserve, exchange rate, and interest
rate differential changes. When the index is above a certain threshold (e.g. two standards
deviation above the mean) the associated period is classified as a speculative attack episode. An
important limitation of the speculative index approach is that the weights assigned to the
components of the index, as well as the threshold used to identify speculative attacks, are largely
arbitrary. Consequently, the methodology used to classify observations may be quite ad-hoc.
Surprisingly, even fewer studies have systematically analyzed the determinants of ERM
currency crises.6 Eichengreen, Rose, and Wyplosz (1995, 1996) and Otker and Pazarba,ioglu
5 As defined earlier, by speculative attacks we mean not only devaluations, but also instances where a devaluation
was averted at the expense of large losses in reserves or interest rate hikes.
6 Studies like Caramazza (1993), Chen and Giovannini (1993), and Rose and Svensson (1994) have analyzed
realignment expectations, but not the more fundamental issue of what determines actual probabilities of attacks or
6
(1997) study speculative attacks and realignments, respectively, in the context of the ERM.7 In
ERW (1995), the authors examine a sample of 20 industrial countries throughout 1959-1993,
including most ERM countries. Speculative attack episodes are identified by an index of
speculative pressure. The authors construct this index as a weighted average of changes in
exchange rates, reserves, and interest rate differentials. The weights assigned to each variable are
such that all variables have the same variance. They use logit analysis to study the determinants
of the probability of speculative attacks. Overall, the authors find that the unemployment rate,
government budget deficit, and domestic credit are unrelated to exchange rate episodes.
ERW (1996) conduct a non-parametric analysis of speculative attacks in 22 countries
during the period 1967-1992. Once again, the authors construct an index of speculative pressure
to identify attacks. This paper finds that the behavior of fundamentals in ERM crises periods is
not significantly different to that in non-crises periods. They interpret this evidence as a
departure from the first generation models of attacks. However, when they examine realignments
only (rather than speculative attacks in general), they find evidence that fundamentals deteriorate
prior to these episodes. They conclude that "governments historically chose to realign ERM
currencies on the basis of standard macroeconomic criteria but that speculators chose to attack
ERM currencies for other reasons".
While the work by ERW mentioned above is very informative, these studies define crises
on the basis of indices which involve a large degree of arbitrariness in their construction. Also, in
analyzing the determinants of currency crises, the authors ignore any direct role that expectations
might play in bringing about speculative attacks.
devaluations.
7 Jeanne (1997) develops a model of a fixed exchange rate in which self-fiulfilling expectations and fundamentals
may complement each other in causing currency crises. The model is estimated specifically for the case of the 1992-
93 crisis of the French franc. The author finds evidence of self-fulfilling speculation at work.
7
Otker and Pazarba,ioglu (1997) use probit analysis to estimate the probability of
devaluations in a sample of ERM countries during the period 1979-1995.8 The authors test for
the role of speculative factors (like interest rate differentials and the position of the exchange rate
within the band) and fundamentals (like domestic credit, budget deficits, trade balances, and
unemployment rates) in bringing about devaluations or realignments of the currencies involved.
Overall, the paper finds that both economic fundamentals and speculative factors contribute to
the devaluations of ERM currencies. This paper takes a step in the right direction by allowing for
the possibility that devaluations result from speculative factors, and not only from deteriorating
fundamentals. However, the analysis in this paper is too restrictive since it only studies
devaluations or realignments. Also, the authors examine the role of speculative factors and
fundamentals separately rather than jointly, as it would be necessary to draw any inferences
regarding their relative impact on the likelihood of devaluations.
The approach developed in the remaining of this paper tries to address some of the
limitations in the existing empirical literature on speculative attacks. First, the methodology
suggested to identify attacks avoids the use of indices of speculative pressure. Second, in
evaluating the likelihood of attacks, this study allows expectations to have a direct role on the
likelihood of switching from periods of tranquility to periods of speculative pressure. In other
words, measures of expectations are included along with economic fundamentals in the
estimation of switching probabilities.
s The countries included are: Belgium, Denmark, France, Ireland, Italy, and Spain.
8
3- The empirical literature on switching models applied to exchange rates
Most of the empirical papers that estimate switching models apply Hamilton's (1990)
switching model or a variant of this model. In Hamilton's framework, time series dynamics are
governed by a finite-dimensional parameter vector, which switches (possibly every period)
depending upon which of two states is realized. State transitions are governed by a first-order
Markov process with constant transition probabilities.9 Diebold, Lee, and Weinbach (1994),
develop a variant of the Markov switching model in which transition probabilities are
endogenous. In other words, in Diebold et al. the probabilities of switching regimes depend on
time-varying regressors.
A number of recent studies have used Hamilton's Markov switching model, or the
Diebold et al.'s variant, to study the behavior of exchange rates.10 Engel and Hamilton (1990)
estimate a two-state Markov switching model with constant transition probabilities to explain the
behavior of the U.S. dollar against the German mark, the French franc, and British pound over
the period 1973-1988. They find that the switching model outperforms the popular random walk
model of exchange rates. Weinbach (1995) extends the work of Engel and Hamilton to the time-
varying transition probability case.
Switching models have also been used to analyze the behavior of semi-fixed exchange
rates. Engel and Hakkio (1996) use Hadi's method of identifying outliers to show that EMS
exchange rates over the period 1979-1993 seem to be drawn from a mixture of distributions, one
9Let St be a random variable that can assume only two integer values {O,1 }. Suppose that the probability that St
equals some particular value j depends on the past only through the most recent value St : P{Stj/S,l=i,St.
2=j.}=PPSt=j/S.1-=i}=pij. Such a process is described as a 2-state first order Markov process.
10 For a discussion on the use of switching models to study business cycles see Filardo (1994) and Ghysels (1994).
Jones (1997) implements a time-varying regime-switching model to study lender of last resort credibility for the
U.S.
" Weinbach allows dollar exchange rate switches between periods of appreciation and depreciation to be a
function of market fundamentals.
9
with high variance (which coincides with speculative attacks and realignments) and one with low
variance. Given this evidence, and since the volatile periods tend to be clustered together, they fit
a regime-switching model to study the behavior of EMS exchange rates. They model the
probability of switching between states as a function of the distance of the exchange rate from
the upper band. They find that the probability of staying in a volatile state increases, the closer is
the exchange rate to the top of the band.
Relative to the papers discussed above, Hsieh (1994) is the most related to this study. He
estimates an AR(4) version of Hamilton's switching model to identify speculative attacks against
EMS currencies in the period 1979-1993. He models the behavior of the components of
Eichengreen et al.'s index of speculative pressure as time series subject to discrete shifts in
regime. That is, exchange rates, reserves, and interest rate differentials of EMS countries are
assumed to switch between periods of tranquility and periods of speculative attacks. As in the
original Hamilton model, he assumes that transition probabilities are constant over time. Attacks
are identified as periods where the probability of a speculative attack is almost one. Hsieh finds
that the switching model captures most of the attacks identified by Eichengreen et al., as well as
actual realignments.
Hsieh's objective is exclusively to identify periods of speculative pressure by using a
regime-switching methodology and to compare these episodes with those captured by ERW's
index. On the other hand, this paper attempts both to identify attacks and to study their
determinants. Similarly to Hsieh, we model the components of the Eichengreen et al.'s index as
autoregressive time series subject to shift in regimes. However, while Hsieh only assumes the
mean and variance to be different across regimes, the models estimated here also allow the
coefficients on the autoregressive terms to be potentially different across regimes. Therefore,
10
these models yield greater flexibility. Also, instead of identifying attacks by fitting only separate
switching models to exchange rates, reserves, and interest rate differentials, this paper estimates
a VAR switching model, which allows for the more realistic case that attacks are associated with
the behavior of the three variables combined. Furthermore, while in Hsieh's study transition
probabilities are constant over time, this paper allows transition probabilities to and from
speculative attack regimes to be driven by both economic fundamentals and expectations. Thus,
this enables us to analyze explicitly the determinants of speculative attacks.
4- Empirical Estimation Approach
Speculative attacks are typically associated with significant depreciations of the exchange
rate. However, attacks can also result in sharp falls of reserves or increases in the interest rate
differential, depending on the policy pursued by the government at the time of the attacks. For
example, the exchange rate might depreciate if the government is not willing to lose reserves or
to raise interest rates to defend the currency. Alternatively, if the government is determined to
maintain a fixed parity or band, it might be willing to sell reserves or even increase interest rates
to deter the outflow of reserves. Finally, if the government is concerned with the impact of high
interest rates on unemployment or on the health of the banking system, it will be forced to see
reserves fall or it will have to abandon its exchange rate objective. Therefore, at a given point in
time, a speculative attack can be associated with a depreciation of the exchange rate, a fall in
reserves, and/or an increase in interest rates. On the other hand, these three variables are typically
stable during periods of tranquility. Consequently, it follows that reserves, exchange rates, and
interest rates exhibit a different behavior in periods of tranquility than in periods of speculative
attacks.
11
This paper identifies speculative attacks by modeling exchange rates, reserves, and
interest rate differentials as time series subject to discrete shifts in regime. In particular, this
paper estimates two regime-switching models to identify attacks and to study the determinants of
switching to speculative regimes. In the first model, attacks are identified by modeling exchange
rates only as time series subject to regime changes. In the second model, we adopt a VAR
switching structure to allow speculative attacks to be associated with regime shifts in reserves,
exchange rates, and/or interest rates differentials. In both models, we assume time-varying
transition probabilities that are logistic functions of fundamentals and expectations.
MIodel 1: Exchange rate switching model
We assume exchange rate changes, Aet, follow the process below 2
Aet = as,o + as,, [Aet - I] + aS,2 [Aet - 2] + as,3 [Aet - 3] + aS,4 [Aet - 4] + U(St) £ (1)
where c t is an i.i.d N(O,1) variable.
Equation (1) states that exchange rate changes behave differently depending on the value
of St. We assume exchange rates changes follow an AR(4) process.13 However, the mean,
variance, and autoregressive parameters of the exchange rate equation depend on the state in
which the economy resides. St is an unobserved zero/one variable which characterizes the regime
the economy is in, and consequently, the process the exchange rate will follow on date t. That is,
there are two possible regimes, a "tranquil" regime (which corresponds to St=O) where the
parameters and the variance of the exchange rate equation are such that the exchange rate
12 The exchange rate variable mentioned above is in fact the percentage change in the exchange rate of each
currency with respect to the Deutsche mark.
3 This follows the analysis conducted by Hsieh (1994).
12
remains stable, and a "speculative attack" regime (where St=l) characterized by large
depreciations of the exchange rate and high volatility.
In Hamilton's framework, St follows a first-order two-state Markov process with
transition probabilities given by:
NS, = I/ Sts = 1) = p (2)
NS , = 0/ S, ] = 1) = I - p] = p° (3)
P(s, = O/s,,1 = ) = p p' (
N(S, = I/ s,,1 = O) = I _ p° = pO (5)
Equations (2)-(5) list the probabilities of being in either of the two states, given the state realized
in the previous period. For example, the probability of a tranquil state (St=O) on date t, given a
tranquil state in the previous period (St-i=O) is a constant poo. Similarly, the probability of a
tranquil state on date t, given a speculative attack in the previous period is a constant plO
A limitation of Hamilton's model is that transition probabilities are fixed. That is, the
probability of a particular state, given the state realized in the previous period, is constant over
time. Thus, we adopt a variant of Hamilton's model developed by Diebold et al. (1994) in which
the transition probabilities given in equations (2)-(5) are time-varying. In particular, these
probabilities are estimated as logistic functions of a conditioning matrix Xt1, as shown in
equations (6)-(9).
00 = exp x,, - (60
P= P(s, = / S,-, = 0,tx,t1,o =) I + exp xt,,,6 (6)
1+exp x,t'
13
Pt = (-p ) = P(s, = P/Si-] = O,X,-,;f0) = ex-xlI+ (8)
1+ exp ,
p, = (I - p' ) P(s, = O/ s,_1 = 1, x,-l;,81) = 1- + expx / (9)
By allowing transition probabilities to vary over time, we can model the mechanics
underlying shifts from tranquil to speculative attack regimes explicitly. In particular, we use this
framework to determine whether economic fundamentals and/or expectations have any effect in
bringing about shifts to speculative attack regimes.
Given the literature on speculative attacks described in section 2, we include the
following variables as potential determinants of the transition probabilities (i.e. the variables to
be included in the matrix X1,1): growth of domestic credit, ratio of imports to exports,
unemployment rate, fiscal deficit, and interest rates. We express all of these variables as
dLifferentials with respect to the corresponding German variable.14 The ratio of imports to exports
is used as a proxy of the current account deficit. We also include a real exchange rate index to
capture the possible deterioration in the competitiveness of a country against Germany. The
interest rate differential is incorporated in the estimation of the transition probabilities as a
measure of expectations. In other words, assuming uncovered interest parity, interest rate
differentials capture expectations of exchange rate depreciations.
Following Diebold et al., we adopt the EM algorithm to obtain maximum likelihood
estimates of all the parameters in model 1. In general, the EM algorithm maximizes the
incomplete-data log likelihood (that for {Aet} alone) via the iterative maximization of the
expected complete-data log likelihood (that for {Aet} and {st}), conditional upon the observable
14 The reason why we measure all variables relative to Germany is because though the EMS during the period
1979-1993 was in principle a symmetric system, Germany was in fact the anchor country. All realignments
within the EMS implied devaluations of the currencies with respect to the Deutsche mark.
14
data. Given the observed data and some initial estimate of the parameters in the model, the EM
algorithm begins by calculating the smoothed state probabilities (i.e. the unconditional
probability of a particular state). With the estimated smoothed state and transition probabilities,
the expected complete-data log likelihood function is constructed. This is the "E", expectation
part of the algorithm. The expected complete-data log likelihood function is then maximized to
obtain an updated parameter estimate. This is the "M", maximization part of the algorithm. Using
this updated estimate, the smoothed probabilities are calculated again and substituted into the
expected likelihood function, which is maximized again. This procedure is repeated until
convergence (in the parameter estimates or the likelihood function) is obtained.15
Model 2: VAR switching model
Though most speculative attacks result in sizeable devaluations, attacks can also be
associated with substantial losses in reserves, and/or increases in interest rate differentials.
Consequently, it is appropriate to identify speculative attacks taking into account the behavior of
these three variables.
In this model, we assume that changes in exchange rates (Aet) , reserves (Art), and
interest rate differentials (Aidt) behave according to the following VAR switching structure:
Ae, = CAe + a, [Ae,, ]+ g,t [Ar,] Si [Aid, l]+(SI)u, (10)
Ar~ = C Ar + a.r [Ae, X]+ asr [Air,] + YS[Aid,I]±+(St)Ut (11)
hid tS -1] [eladx[ ,]+Ai [Aid, _j+a(S.)u,' (12)
See Diebold et al. (1994) for a detailed description of the EM algorithm and for the closed form solutions of
the maximum likelihood estimates of the parameters.
15
where utAe, UtAr, and UtAid are i.i.d. N(0,1) variables and the superscripts Ae, Ar, and Aid indicate the
equation to which the parameters belong to.
Once again, St is an unobserved zero/one variable that characterizes the regime in which
the process is in, on date t. As before, there are two possible regimes: a "tranquil" regime and a
"speculative attack" regime that is characterized by large depreciations of the exchange rate,
sharp falls in reserves, andlor increases in the interest rate differential. The process described in
equations (10) through (12) differs from a standard VAR(1) specification in that the constant
term, the pararmeters on the lagged values of exchange rates, reserves, and interest rate
differentials, as well as the error term variances, are functions of the regime at the time. Thus, we
can think of the VAR above as really two VARs, one that holds when St=O and one that
determines, Aet, Art, and Aidt when St=1.
Similarly to the exchange rate switching model, we allow transition probabilities between
periods of tranquility and speculative attacks to vary over time. These probabilities follow
equations (6) through (9) above. In fact, we model these probabilities to be a function of the
same Xt-l matrix that determines transition probabilities in the exchange rate switching model. In
other words, we assume that transition probabilities are a function of differential (domestic
minus German) growth of domestic credit, ratio of imports to exports, unemployment rates,
fiscal deficit as a percentage of GDP, and interest rates. Also, a real exchange rate index is
included to capture changes in competitiveness.
The parameters in this model are also estimated using the EM algorithm outlined above.
For computational convenience, equations (10)-(12) are transformed using a Cholesky
decomposition so that the model's variance-covariance matrix becomes diagonal. This allows the
likelihood function for the model to be the product of the likelihood functions of each of the
16
three equations. Given that we are not concerned with the actual parameters in these equations,
but only with the fact that the parameters in each equation are different across states, this
transformation is harmless. Since there is a one to one relationship between the triangular and
non-triangular versions of the VAR, and given that the coefficients in the transition probabilities
are the same in both normalizations, their estimates are unaffected.
5- The Data
We use the empirical methodology described above to identify and to study the nature of
speculative attacks in ERM member countries between 1979 and 1993. The following countries
are included in the sample: Belgium (1979-93), Denmark (1979-93), France (1979-93), Ireland
(1979-93), Italy (1979-92), Spain (1989-93), and the UK (1990-92). Most variables for these
countries are measured as differences or ratios to the corresponding German values.
Monthly data for this study were obtained from the IMF's International Financial
Statistics and the OECD's Main Economic Indicators publications. Survey data on expected
exchange rates came from the Currency Forecasters Digest, a Financial Times publication.
6- Empirical results
The results in this paper, both for the exchange rate and the VAR switching models, are
obtained by pooling the data for all countries in the sample. The observations considered for each
country span the period since the respective country joined the ERM through August 1993, when
the ERM band was significantly widened. Pooling the data is necessary because the number of
switches (or speculative attacks) for each country is small relative to the number of parameters to
be estimated in a model where transition probabilities are time-varying. In other words, given a
17
limited number of switches per country, it is impossible to estimate transition probabilities as a
function of multiple regressors unless we pool the data.
Results from the exchange rate switching model
Table 1 contains the parameter estimates of the switching model based on monthly
percentage changes in the exchange rate during the period 1979-1993. Model (1.1) presents the
estimates of the constant transition probability version oiF the exchange rate switching model (i.e.
the Hamilton version model). Model (1.2) refers to the estimates from the exchange rate
switching model with time-varying transition probabilities. This is Diebold et al.'s variant of the
switching model. As noted earlier, the main reason to estimate this model is to allow for the
possibility that the probability of switching from a period of tranquility to a speculative regime
may be driven by fundamentals and/or expectations. In these estimations, the interest rate
differential is used as a proxy of exchange rate expectations. Model (1.3) is the same as (1.2)
except that we include dummy variables to control for country fixed effects.
We estimate model (1.1), the Hamilton version of the exchange rate switching model, to
test two hypotheses. The first one is the null of no switc:hing. We perform this test by comparing
the log likelihood function from a model where the autoregressive parameters are constrained to
be equal across states against the alternative shown in model (1.1), where we allow these
parameters to be different across states.1617 aOO-ao4 refer to the constant and autoregressive
16 The problem of testing the hypothesis of no switching is that under the null the parameters in the transition
probabilities become nuisance parameters. When this happens, we cannot assume the standard distributions to
conduct our tests. To circumvent this problem we follow Engel and Hamilton (1990) in their "approximate" test
of no switching. What the authors do (and we follow them) is to testi whether the autoregressive parameters are
the same across states, while allowing the variance of exchange rates to be different in different states. We also
conduct the opposite test, where the autoregressive parameters change across states, but the variances are
assumed constant across regimes.
17 We do not show the results for the Hamilton version of the model where the autoregressive parameters are
18
parameters for the tranquil state, while co-It14 are the corresponding parameters for the
speculative state. At 5 percent significance, we reject the hypothesis of equal alpha parameters
across states. That is, we reject the null that aOO-CCO4 =alOI-a4.18 This provides evidence that the
exchange rate is drawn from two regimes as we assume.
The second hypothesis we test, involving the Hamilton version of the model, is whether
fundamentals and expectations play a significant role in determining transition probabilities. The
null hypothesis considered (i.e., POI-O6=P1I 1-P16=0 from equations (6)-(9)) is a test of the validity
of the Hanilton constant probability version of the model (i.e., model (1.1)) versus Diebold et
al.'s time-varying transition probability specification (i.e., model (1.2)). At 5 percent
significance, we reject the null that the constant transition probability model is the true model.'9
Therefore, the results indicate that fundamentals and expectations significantly affect the
likelihood of switching to and from speculative attack periods.
Regarding the transition probabilities' parameters, bOO-P06 enter with a positive sign in the
equation for Ptoo, the probability of staying in a tranquil state given that the previous period was
also a tranquil one (see equation (6)). On the other hand, P00-P06 negatively affect the
complement of pt°°, Pt01, the probability that a speculative attack will follow a period of
tranquility (see equation (8)). The results in model (1.2) indicate that an increase in the trade
deficit, an appreciation in the real exchange rate, and an increase in the interest rate differential
have a negative impact on the probability of staying in a period of tranquility. Alternatively, an
constrained to be equal across states because we do not have an interest in these parameters individually. The
only reason behind the estimation of this model for our purposes is to test the hypothesis of no switching.
18 The likelihood ratio test statistic is 54.52. The 5 percent significance level critical chi value for this test is 9.49.
Therefore, we reject the null of no switching at 5 percent significance.
19 The likelihood ratio test statistic is 51.97. The 5 percent significance level critical chi value for this test 21.83.
Therefore, the null is rejected at 5 percent significance level.
19
increase in these variables has a positive effect on the probability of switching from a period of
tranquility to a speculative attack. Only the interest rate differential has a statistically significant
effect. Since the interest rate differential is a proxy for expectations, the fact that this variable is
significant indicates that agents' expectations of devaluations can play an important role in
causing speculative attacks against a currency. This result is consistent with the predictions from
models of self-fulfilling attacks.
The growth of domestic credit, the unemployment rate, and the government surplus have
a positive effect on the probability that a state of tranquility will follow a period of tranquility, or
alternatively, a negative impact on the likelihood of switching from a period of tranquility to a
speculative regime. The first two variables are insignificant and have opposite signs to those
predicted by a model of speculative attacks caused by fundamentals. On the other hand, the
government surplus as a percentage of GDP has the expected sign and is individually statistically
significant. As predicted by the first generation models, the results indicate that larger
government deficits increase the probability of switching from periods of tranquility to
speculative attack episodes.
The parameters P10-P16 in model (1.2) affect pt", the probability that a speculative state
will follow a speculative state, and its complement, Ptl', the probability of switching to a tranquil
state given a speculative attack in the previous period. No regressor affecting pt1' and therefore,
Ptl, iS significant. A possible explanation for this result (which will be illustrated in section 7) is
that speculative attacks are rare, short-lived events. Therefore, with very few and brief
speculative episodes, it is hard to estimate the significance of any particular parameter.
[Table 1 here]
20
Model (1.3) in Table 1 presents the results obtained when we include dummy variables in
the estimation of the transition probabilities to control for country fixed effects. Therefore, model
(1.3) is identical to model (1.2), with the exception that country dummies are added to this latter
specification. Given that no dummy is individually significant, we do not report their coefficient
estimates here. However, at 5 percent significance, a likelihood ratio test rejects the null that
country-fixed'effects are jointly zero.20
When dummies are included, the parameter estimates for the transition probabilities do
not change significantly. As in model (1.2), only the interest rate differential and the government
surplus (as a % GDP) have a significant impact on ptoo and its complement, pt0l, the likelihood of
switching from a period of stability to a speculative attack. The interest rate differential has a
negative impact on pt°° and, consequently, as expected, a positive impact on Pt01. Thus, the larger
the interest rate differential, the greater the expectations that a speculative attack might occur,
and, therefore, the larger the probability of switching from a tranquil period into a speculative
attack regime. On the other hand, the larger the government surplus, the smaller the probability
of switching from a period of tranquility to a speculative regime.
Results from the VAR switching model
Table 2 below presents the estimates from the VAR switching model outlined in
equations (10)-(12) plus the transition probabilities parameters of equations (6)-(9). For
computational convenience, we estimate the Choleski transformed version of equations (10)-
(12).
20 The likelihood ratio test statistic is 46.5. The 5 percent significance level critical chi value for this test is 23.69.
Therefore, the null is rejected at 5 percent significance.
21
The VAR switching model allows us to identify and examine the determinants of crises
defined in a broader sense than in the exchange rate switching model. In the case of the latter, we
identified as speculative attacks episodes, periods of large depreciations or realignments of the
exchange rate. With the VAR switching model, we identify as speculative attacks, periods of
large depreciations of exchange rates, large drops in reserves, and/or significant interest rate
increases. Thus, in the VAR model, we allow for the possibility that countries defend from
pressures to devalue their currencies by raising interest rates or selling reserves.
Model (2.1) in Table 2 presents the Hamilton version of the VAR switching model where
transition probabilities are constant across regime. Model (2.2) corresponds to the estimation of
the VAR switching model with time-varying transition probabilities. Model (2.3) is identical to
model (2.2) except that the former includes country dummies.
The purpose of estimating the Hamilton version of the VAR switching model (model
(2.1')) is twofold. In the first place, we estimate this model to test the null of no switching in
exchLange rates, reserves, and interest rate differentials.21 In other words, we conduct a likelihood
ratio test to compare model (2.1) against a specification where we constrain the parameters in the
VAR equations to be equal across states.22 At 5 percent significance, we reject the null
hypothesis of no switching.23
Secondly, we estimate model (2.1) to test the null hypothesis of constant transition
probabilities against the preferred hypothesis that transition probabilities vary over time. This
involves testing model (2.1) against model (2.2). At the 5 percent significance level, we reject
21 The test conducted here is only an approximation to the true test of no switching. See footnote 16.
22 The results from the constrained VAR switching model are not shown here, but are available upon request.
23 The likelihood ratio test statistic is 45.34. The 5 percent significance level critical chi value for this test is 25.
Therefore, the null is rejected at 5 percent significance level.
22
the null hypothesis of constant transition probabilities.24 Thus, the time-varying transition
probability model, i.e., model (2.2), is better suited than the Hamilton model to study speculative
attacks and their determinants.
[Table 2 here]
Regarding the determinants of the transition probabilities in model (2.2), at 5 percent
significance, we can reject the hypothesis that these are jointly insignificant. Thus, fundamentals
and expectations determine the likelihood of switching from and to periods of speculative
attacks. However, no variable is individually significant.
Model (2.3) is the same as model (2.2) with the exception that the former includes
country dummy variables to control for country fixed effects. We find that country dummies are
jointly significant, but none of them are individually significant.25 Once we allow for country
fixed effects, we find that the government budget deficit plays a significant role in determining
the likelihood that a country will switch from a state of tranquility to a speculative attack period.
Finally, Table 3 below compares the result from the estimation of the VAR switching
model using survey data on expected exchange rates, with the results using interest rate
differentials to capture expectations. The purpose of this exercise is to analyze whether the
results we obtained regarding the determinants of the transition probabilities are sensitive to the
choice of expectations proxy we use. Because survey data on expected exchange rates is
available since February 1988, we can only estimate the model for the period 1988-1993. Given
that we are primarily interested in the effect that expectations have on the probabilities of
24 The likelihood ratio test statistic is 23.82. The 5 percent significance level critical chi value for this test is
21.03. Therefore, the null is rejected at 5 percent significance.
25 The likelihood ratio test statistic is 40. The 5 percent significance level critical chi value for this test is 23.64.
Therefore, at 5 percent significance we reject the null that the country dummies are jointly zero.
23
switching to and from speculative regimes, we only report the parameters affecting these
probabilities.
[Table 3 here]
Both the interest rate differential and the expected exchange rate have a negative impact
on the probability of staying in a period of tranquility or, equivalently, a positive effect on the
probability of switching from a period of tranquility to a speculative attack. However, neither of
these two expectations proxies are significant. Also, no other variable is individually significant
in explaining switching probabilities for the period 1988-1993, although once again we find that
fundamentals and expectations are jointly significant.
The fact that nothing seems to be explaining the EMS crises over the period 1988-1993 is
consistent with what other authors have found. Rose and Svensson (1994), for example, find that
realignment expectations remained fairly constant throughout this period. Similarly, Eichengreen
and Wyplosz (1993) argue that there is weak evidence that the 1992-93 EMS crises were the
result of deteriorating economic fundamentals.
Finally, there can be a purely statistical reason explaining why no variable has an
individually significant effect on the likelihood of switching to a speculative attack during 1988-
93. Given that this period was in general a tranquil one, with the exception of the 1992-93 crises,
it is possible that there are only a few regime switches in the data to identify all the parameters in
the model. This may lead to inefficient estimates and, therefore, may explain why no regressor is
individually significant during this period.
24
7- How well do the switching models with time-varying transition probabilities track
speculative attacks?
From the estimation of the exchange rate and the VAR switching models, it is possible to
recover the estimated probability of a speculative attack at each point in time, given the
observable data.26 Figures A.l.a-A.14.a included in the appendix, display the unconditional
smoothed probabilities that the economy resided in a speculative state according to the exchange-
rate (figures A.l.a-A.7.a) and VAR (figures A.8.a-A.14.a) switching models. Figures A.l.b-
A. 14.b in the appendix show the estimated probability of switching from a period of tranquility
to a speculative regime. These correspond to the transition probability labeled ptol in equation
(8).
From Figures A.l.a-A.14.a, it is clear that most of the estimated probabilities of
speculative attacks are either close to zero or one. Also, these probabilities move rapidly between
the two extremes. This seems to ratify the conventional wisdom that speculative attacks appear
suddenly and are short-lived events. From figures A. l.b-A. 14.b, we see that the likelihood of
switching to a speculative attack increases prior to attacks.
Tables A. 1 -A.7 in the appendix compare, for each country, the speculative attacks
episodes identified by the exchange rate switching model, with the crises identified by the VAR
switching model, and the attacks captured by Eichengreen et al.'s index of speculative pressure.27
In the case of the exchange rate and the VAR switching models, we assume the process switched
to a speculative attack state when the predicted probability of a speculative attack is greater than
0.5.28 So a "Yes" for the exchange rate switching model and the VAR switching model signifies
26This is the so-called "smoothed" probability of a given regime.
27 For both the VAR and the exchange rate switching models, we refer to the time-varying transition probabilities
versions of these models.
28 Since the estimated probability takes values close to zero or one, the classification of observations does not
change significantly if we choose 0.6, 0.7, or 0.8 as a threshold.
25
that the probability of an attack predicted by each of these models is larger than 0.5. A "Yes"
under the ERW column marks those episodes identified by Eichengreen et al.'s index of
speculative pressure. Finally, the last column in these tables shows the dates when realignments
of each ERM currency took place.
A number of well-established speculative attack episodes appear in the tables. The
September 1992 ERM crisis and the speculative pressure that resulted in the widening of the
ERM band in August 1993 show up for almost every country. Other dates which appear as
episodes of speculative attacks, for at least half of the countries, correspond to the realignments
of October 1981, March 1983, and January 1987. However, from the experiences of most
countries, it is clear that speculative attacks need not always coincide with realignments.
We seek to address two questions regarding the speculative attacks episodes identified by
the regime-switching methodology implemented in this paper. First, can this methodology
capture those episodes identified by ERW's index of speculative pressure? Second, what other
episodes does this methodology pick up and does it make sense to label them speculative attack
episodes?
Table 4 below attempts to answer the first question posed above. This table summarizes
the findings from tables A. 1 -A.7 in the appendix. For each country, table 4 shows the percentage
of episodes identified by ERW that the VAR and exchange rate switching models identify. In
summary, table 4 shows that, with the exception of Denmark, the VAR switching model can
perfectly zapture the episodes identified by ERW's index. On the other hand, the exchange rate
switching model can only fully predict those episodes identified by ERW for the cases of Italy,
Spain, and the UK. Both the VAR model and the ERW index incorporate the behavior of
exchange rates, reserves, and interest rate differentials, while the exchange rate switching model
26
only captures attacks that resulted in depreciations of the exchange rate. Most speculative attacks
against Italy, Spain, and the UK resulted in depreciations of the exchange rate. Consequently, it
is not surprising that for these countries the exchange rate switching model can identify all the
attacks identified by ERW.
Table 4 enables us to verify that the VAR switching model can almost perfectly capture
those episodes identified by the speculative pressure index methodology used by ERW. The next
question is what else does the VAR switching methodology capture? Do these results make
sense? In order to answer these questions, the first column in table 5 lists the number of episodes
captured by the VAR switching model that are not captured by ERW's index of speculative
pressure. The second column in that table reports the number of episodes listed in the first
column for which we could find evidence of speculative pressure, on the relevant currencies, in
the month in questions, in the financial press, or in IMF publications. Tables A.8 through A.14 in
the appendix contain a list of all episodes identified by the VAR switching model followed by a
description of events surrounding them. The main purpose of Table 5 and tables A.8-A.14 is to
show that indeed most of the episodes the VAR switching model identifies can be corresponded
with accounts of speculative pressure in the press or in the IMF's reports.
Table 5 shows that out of 25 attacks not identified by ERW, but captured by the VAR
switching model, there are only 4 cases for which we cannot find evidence of speculative
pressure in the news reports searched. Thus, the VAR regime-switching model can identify most
of ERW's attacks plus others for which we find evidence in the news.
[Table 4 and 5 here]
27
8- Conclusions
This paper implemented a regime-switching approach with time-varying transition
probabilities to study speculative attacks against EMS currencies. We estimated two switching
models: an exchange rate and a VAR switching model. In the exchange rate switching model,
speculative attacks are associated with large depreciations of the exchange rate. In the VAR
switching model, speculative attacks may manifest themselves as depreciations of the exchange
rate, large reserves losses, and/or significant increases in interest rate differentials.
The switching models allow the data to determine the state in which the economy resides
at each point in time. Thus, these models avoid the arbitrary classification of observations that
result from using indices of speculative pressure. Also, the estimation of the switching models
enable us to study the factors affecting the likelihood that a country will shift from a state of
tranquility to a speculative attack. Finally, contrary to other studies that neglect the role of
expectations in causing speculative attacks, this paper incorporated them explicitly.
A number of conclusions follow from the results obtained in this paper. In the first place,
the switching models with time-varying transition probabilities capture most of the conventional
episodes of speculative attacks. Secondly, speculative attacks do not always coincide with
currency realignments. Finally, both economic fundamentals and expectations determine the
likelihood of switching from a period of tranquility to a speculative attack. In particular, in both
switching models, the budget deficit appears to be the most important factor driving the
probability of switching to a speculative regime.
There are a number of potential extensions to this paper. Two are particularly relevant.
Given the importance of anticipating and wherever possible avoiding crises, a useful extension of
this paper would be to conduct forecasting exercises to determine whether the switching
28
framework proposed here can accurately forecast crises out of sample. Finally, given that
currency crises tend to occur at the same time in more than one country, it would be interesting
to adapt the regime-switching framework to explore the role of contagion in explaining crises.
29
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33
Table 1 - Exchange Rate Switching Model Estimates, 1979-1993
Variables Model (1.1) Model (1.2) Model (1.3)*
Autoregressive Parameters Coeff. (T-stat.) Coeff. (T-stat.) Coeff. (T-stat.)
Tranquil State (TS)
aOO- Constant 0.024 (1.84) 0.024 (1.85) 0.023 (1.77)
aO I - Ae,_l 0.148 (7.65) 0.148 (7.92) 0.15 (8.94)
aO2- Aet2 -0.013 (-0.84) -0.012 (-0.74) -0.013 (-0.83)
aO3- Ae-3 0.034 (2.21) 0.033 (2.01) 0.035 (2.08)
aO4- Aet4 -0.001 (-0.04) 0.002 (0.12) 0.003 (0.16)
aO 0.108 (18.34) 0.099 (18.5) 0.097 (18.51)
Speculative State (SS)
aIo- Constant 1.357 (4.42) 1.197 (4.43) 1.173 (4.03)
o l1- Ae,1 0.068 (0.91) 0.10 (1.38) 0.128 (1.59)
a12- Aet-2 -0.187 (-0.76) -0.194 (-0.93) -0.177 (-0.83)
al3-Ae,3 0.291 (1.66) 0.266 (1.88) 0.283 (1.95)
a 14- Aet4 -0.301 (-2.72) -0.292 (-2.71) -0.298 (-2.69)
al 3.679 (8.89) 3.459 (8.25) 3.474 (7.05)
Transition Probabilities Parameters
Iranquil State (TS)
PO0- Constant 2.548 (11.98) 6.261 (2.81) 12.715 (2.85)
D01- Domestic Credit Growth - 0.065 (0.7) 0.133 (1.25)
302- Trade Balance -0.886 (-1.62) 1.361 (0.61)
f303- Real Exchange Rate -0.03 (-1.41) -0.045 (-1.35)
304- Interest Rate Differentials - -0.1 (-1.99) -0.161 (-2.25)
,05- Unemployment Rate 0.069 (0.94) -0.097 (-0.58)
306- Government Surplus 0.11 (2.82) 0.339 (2.86)
Speculative State (SS)
110- Constant 0.211 (0.76) 5.243 (1.48) 2.399 (0.21)
,111- Domestic Credit Growth - -0.149 (-0.95) 0.026 (0.14)
f12- Trade Balance 0.96 (0.79) -1.835 (-0.67)
113- Real Exchange Rate -0.053 (-1.52) -0.01 (-0.19)
f314- Interest Rate Differential - -0.061 (-0.52) -0.126 (-0.65)
115-Unemployment Rate 0.203 (1.56) -0.378 (-1.03)
P1I6- Government Surplus 0.094 (1.31) 0.192 (0.79)
* Note model (1.3) was estimated including country dummies. We do not include these parameters since
none of them is individually statistically significant.
Table 2 - VAR Switching Model Estimates, 1979-1993
Variables Model (2.1) Model (2.2) Model (2.3)*
VAR Parameters Coeff. (T-Stat.) Coeff. (T-Stat.) Coeff. (T-Stat.)
Exchange rate equation
Tranquil State
CO' - Constant 0.058 (3.67) 0.054 (3.42) 0.055 (3.52)
ac*W'-Ae,. 0.046 (2.65) 0.06 (3.28) 0.051 (2.65)
S "' - Ar'' l0.0004 (0.21) 0.001 (0.48) 0.001 (0.32)
y-&""- Aid,., 0.133 (12.39) 0.135 (12.19) 0.132 (10.86)
Speculative State
CW'0 - Constant 0.774 (4.16) 0.789 (4.19) 0.79 (3.92)
a*"' -Ae.1 0.254 (3.64) 0.218 (3.08) 0.214 (2.90)
8*" - Ar, l -0.008 (-0.85) -0.009 (-1.01) -0.009 (-0.89)
y" -_ Aidt, 0.044 (1.35) 0.044 (1.37) 0.044 (1.29)
Reserves Equation
Tranquil State
cO-ht_ Constant 1.095 (4.2) 1.092 (4.18) 1.133 (4.29)
a(O'O - Ae, -3.108 (4.51) -2.98 (4.32) -3.087 (4.49)
a001 - Aet. -0.737 (-2.17) -0.647 (-1.82) -0.72 (-1.97)
*&'- A,,-, 0.035 (1.15) 0.037 (1.22) 0.033 (1.089)
y - Aid,-, -1.03 (-3.16) -1.054 (-3.17) -1.058 (-2.98)
Speculative State
cO'A - Constant 1.417 (0.8) 1.447 (0.8) 1.26 (0.68)
ce00' An_e, -1.374 (-1.72) -1.374 (-1.65) -1.41 (-1.52)
aOl-" - Ae . -0.089 (-0.06) -0.153 (-0.103) -0.157 (-0.1)
8*"' - Art-, -0.301 (-3.79) -0.296 (-3.67) -0 289 (-3.51)
y.W - Aid,_1 0.347 (0.87) 0.358 (0.89) 0.344 (0.86)
Interest rate differential Equation
Tranquil State
cO - - Constant -0.064 (-3.38) -0.061 (-3.26) -0.062 (-3.22)
00"'- - Ae 0.181 (3.69) 0.181 (3.65) 0.183 (3.64)
aolId - Ae., -0.008 (-2.71) -0.007 (-2.56) -0.007 (-2.49)
Soo-Ad -Ar, 0.011 (0.42) 0.015 (0.53) 0.027 (0.93)
SOI Aid - Art-I -0.008 (4.39) -0.007 (-3.99) -0.008 (-4.08)
y,dt -Ai,- 0.078 (4.13) 0.078 (4.04) 0.084 (3.77)
Speculative State
cOAdl' -Constant 0.48 (1.38) 0.456 (1.28) 0.467 (1.31)
cOO0" - Aet -0.216 (-0.9) -0.207 (-0.85) -0.203 (-0.83)
aOl d'- Ae1. -0.0188 (-1.53) -0.018 (-1.48) -0.018 (-1.42)
SOO Adt- Ar1 0.169 (0.49) 0.143 (0.41) 0.119 (0.38)
601 - Ar1,. -0.029 (-1.54) -0.031 (-1.58) -0.031 (-1.61)
y*idt -Ai -0.769 (-12.41) -0.768 (-11.88) -0.763 (-10.78)
Transition Probability Param.
Tranquil State
100 - Constant 2.276 (14.22) 4.526 (2.75) 5.266 (2.19)
,301 - Domestic Credit 0.011 (0.14) 0.017 (0.19)
P02 - Trade Balance 0.556 (1.13) -0.864 (-0.53)
1303-Real ExchangeRate -0.017 (-1.13) -0.029 (-1.4)
!304 - Interest Rate Differential -0.087 (-1.27) 0.005 (0.06)
105 - Unemployment Rate 0.065 (1.18) 0.171 (1.66)
06 - Government Surplus 0.072 (1.55) 0.254 (2.95)
Speculative State
110 - Constant 0.54 1 (2.93) 1.521 (1.04) -6.632 (-1.83)
1311 - Domestic Credit -0.044 (-0.55) -0.073 (-0.86)
112 - Trade Balance -0.367 (4.61) -3.12 (-1.57)
13 3- Real Exchange Rate -0.01 (-0.73) 0.039 (1.45)
114 - Interest Rate Differential 0.008 (0.11) -0.011 (-0.12)
115-UnemploymentRate 0.119 (1.54) 0.13 (0.9)
116 - Government Surplus 0.082 (1.56) 0.079 (0.79)
For computational convenience, the exchange rate, reserves, and interest differential equations correspond to
the Choleski transformed version of the model. - Model (2.3) was estimated including country dummies.
Table 3 - VAR Switching Model Estimates, 1988-1993
Variables Model (3.1)* Model (3.2)**
Transition Probabilities Param. Coeff. (T-Stat.) Coeff. (T-Stat.)
Tranquil State (TS)
P00- Constant -58.979 (-0.39) -28.76 (-0.55)
1301- Domestic Credit Growth 0.016 (0.06) 0.019 (0.07)
1302- Trade Balance -1.277 (-0.08) -1.065 (-0.07)
1303- Real Exchange Rate 0.569 (0.38) 0.27 (0.56)
P04- Expectations Proxy -0.257 (-0.67) -0.033 (-0.34)
305- Unemployment Rate 1.581 (0.36) 1.359 (0.39)
f06- Government Surplus 0.797 (0.3) 0.9 (0.24)
Speculative State (SS)
PI10- Constant -30.776 (-1.18) -27.911 (-1.29)
PI 1- Domestic Credit Growth 0.106 (0.19) 0.113 (0.21)
312- Trade Balance 0.804 (0.22) 0.773 (0.33)
,13- Real Exchange Rate 0.337 (1.25) 0.309 (1.41)
14- Expectations Proxy 0.06 (0.06) -0.014 (-0.95)
315- Unemployment Rate 0.469 (0.85) 0.616 (1.65)
316- Government Surplus 0.002 (0.01) 0.067 (0.27)
* Note model (3.1) includes the interest rate differential as a proxy for expectations.
* * Note model (3.2) includes expected exchange rates (from survey data) as a proxy
for expectations.
Table 4: ERW's Episodes Captured by the Exchange Rate and VAR Switching Models
Countries % of ERW 's episodes identified % of ERW's episodes identified
by the exchange rate switching model by the VAR switching model
Belgium 33% 100%
Denmark 50% 75%
France 75% 100%
Ireland 60% 100%
Italy 100% 100%
Spain 100% 100%
UK 100% 100%
Table 5: Number of Attacks not in ERW Identified by the VAR Switching Model
Countries # of attacks not in ERW identified # of attacks not in ERW identified
by the VAR switching model by the VAR model for which
there is evidence in the news
Belgium 9 9
Denmark 3 3
France 2 2
Ireland 0 0
Italy 8 6
Spain 2 1
UK I 0
Appendix Figures and Tables
Figure A.I.a* Figure A.I.b**
Belgium - Probability of a Belgium - Probability of Switching to
Speculative Attack a Speculative Attack
1 2 Exchange Rate Switching Model 0.12 Exchange Rate Switching Model
08-tl 0.08
04 0.04
02 0.02
r - '4 Q Q CC Q Q O _ e C - C C C C z C _
C_ C C C _ C C eLL LCL_
Figure A.2.a* Figure A.2.b**
Denmark - Probability of a Denmark - Probability of Switching to
Speculative Attack a Speculative Attack
Exchange Rate Switching Model Exchange Rate Switching Model
08 __ 0.16-
06 ~~ ' t1lik * & ji 0' l
-S C- Q C I _t C'n g_c I - DC-S
I C S I G I =C I I I GC I (C ,
C) C C G(C C) GC c; C C DC C) U
Figure A.3.a* Figure A.3.b**
France - Probability of a France - Probability of Switching to a
Speculative Attack Speculative Attack
Exchange Rate Switching Model 0 14 Exchange Rate Switching Model
129 - 0.12 _
0-6.~~~~~~~~~~~~~~~~~~.
0.8~~~~~~~~~~~~~~~~.8
06~~~~~~~~~~~~~~~~~
0.4 00
0 0
CCigursc - probability of c a t cance rat swiing mof d c
**FigSres display the estimated probability ofAa sSpeculative a cr t n atwtachngie acmrde
to the exchange rate svitchiEng model.
Figure A.4.a* Figure A.4.b**
Ireland - Probability of a Ireland - Probability of Switching to a
Speculative Attack Speculative Attack
Exchange Rate Switching Model Exchange Rate Switching Model
0.45
1.2 0.4
1 0.35
0.3
0.8 - ~~~~~~~~~~~~~~~~0.25
0.6 0.2
0.4 0.15
0.2 -0.05 ..-
00 - - 0 - 0 -b 0 ,C;UoC
Figure A.5.a* Figure A.5.b**
Italy- Probability of a Italy - Probability of Switching to a
Speculative Attack Speculative Attack
Exchange Rate Switching Model Exchange Rate Switching Model
1.2 0.45
0.35
0,8 0.3
o0 - I 00 4n 00 0r I _ C D - C02 CO CO I} C 04
00 00 00 6 00 00 00 00 2500 o 00
0. 0 0 I I o
0.20 00 C 00 . =
Figure A.6.a* Figure A.6.b**
Spain- Probability of a Spain - Probability of Switching to a
Speculative Attack Speculative Attack
Exchange Rate Switching Model Exchange Rate Switching Model
1.2 0.14
1 0.12
0.8 0.81
0.6 o.o6
0.4 0.04
0.2 0.02-
06- 0 - 40 04il 00 00 00 00 a 00 00 00 00 00
-O 00 00 a 00 00 00 00 00 o I 0 \2
a a 0H0 H H 00HH0 H HHHH H-H0
'Figures display the estimated probability of a speculative attack according to the exchange rate switching model.
" *Figures display the estimated probability of switching from a tranquil period to a speculative attack regime according
to the exchange rate switching model.
Figure A.7.a*
UK- Probability of a Speculative Attack
Exchange Rate Switching Model
1.2
0.8
0.6
0.4
002
0
002 i2 0 2 0 2 0 2 0 20 2 0 2 0
0.07
0 060 0 0 2 ~ 0 0 00
0 : \ ? 22 05 0 22
-2 r -0 02 -0 r 02 -~ 2 0 2 0 -~ - _2 0
-Figure displays the estimated prbability Ff a specularve anack according to the exchange rate switching modeL
"UFigure displays the estimated probability of switching from a tranquil period to a speculative aAtack regime according
to the exchange rate switching model.
Figure A.8.a* Figure A.8.b**
Belgium- Probability of a Belgium - Probability of Switching to
Speculative Attack a Speculative Attack
VAR Switching Model VAR Svitching Model
1.2
1 0.4
0. 0.25
0.2
0.4 0.15
0. O'
0.05
-, C5-O c oZ -~ Za _, O :OC - ~ - Z
Figure A.9.a* Figure A.9.b**
Denmark - Probability of a Denmark - Probability of Switching to
Speculative Attack a Speculative Attack
VAR Switching Model VAR Switching Model
1.2 0.2
0. 0.15
i86 ~ ~ ~ ~~ F0.15
O 4
0~~~~~~~~~~~~~~~
- z O w ; - > : S
Figure A.10.a* Figure A.10.b**
France - Probability of a France - Probability of SwPitching to a
Speculative Attack Speculative Attack
VAR Switching Model VAR SwFitching Model
1.2
o 1 0.2,
0.4~ ~ ~ ~ ~~~~~~~~~~~00
O0
oz~~a a, P' c. a,r - I>. a, a cs rD>e a7
>~~~~~~~a o o3 o, o 0 a, a, o, - :o a, O : o
*Figures display the estmated probability of a speculative attack accoTding to the VAR switching model.
SFigures display the estimated probability of sSvitching from a tranquil period to a speculabve attack regime according
to the VAR switching model.
Figure A.1l.a* Figure A.1l.b**
Ireland - Probability of a Ireland - Probability of Switching to a
Speculative Attack Speculative Attack
VAR Switching Model VAR Switching Model
12 0.4
uhf-I ~~~~~~~~~~~~~~0.2
0.6 | l j l 111 0.3
0 4 UI 0.1
02 2 2 2 2 2 2 2 22
Figure A.12.a* Figure A.12.b**
Italy - Probability of a Italy - Probability of Switching to a
Speculatve Attack Speculative Attack
VAR Switching Model VAR Switching Model
12 0.35
1 ~~~~~~~~~~~~~~~~0.3
08 II i 0.25
0 6 0.2
0.2 110.1 l .
0 ~~~~~~~~~~~~~~~0.0
cm cm cm c -5 C5 CC S C 5 5 C O- -'55
F JGn - - ° 3s z Q - 2
Figure A.13.a* Figure A.13.b**
Spain - Probability of a Spain - Probability of Switching to a
Speculative Attack Speculative Attack
VAR Switching Model VAR Switching Model
12 0.25
1 ~~~~~~~~~~~~~~~~~0.2
0.8 0.15
t 6 ft01
0.4
0.2 ~I I0.05
5c , - - Cm} Cm? cm cmD C - - _c G' y=-
5i _5C C i 5
-, 2 aC__ - O _ -G
'Figures display the estimated probability of a speculative attack according to the VAR switching model.
"*Figures display the estimated probability of switching from a tranquil period to a speculative attack regime according
:o the VAR switching model.
Figure A.14.a*
UK - Probability of a Speculative Attack
VAR Switching Model
1.2
0.8
0.4
0.2 __
0 __ _
VAR Switching Model
C_ C_ C_ - _ CS CS CS - _ CS CS CS CS2 CS2 CS CS
- _ (> - - CC CS C: C. =S C _S = -00;2
0) -C __ u- c ( C, ,C ( C cS 5C _C _
*Figure displays the estimated probability of a speculative attack according to the VAR switching model.
Figure displays the estimated probability of switching from a tranquil period to a speculative attack
regime according to the VAR switching modelV
Table A.1.
Speculative Attack Episodes: Belgium
Dates of Attacks identified Attacks identified Attacks identified Realignment
Speculative Attacks by the exchange rate by the VAR by ERW's index Dates
switching model switching model of speculative pressure
Sept. 1979 - May 1980 No Yes No 1979:09:24
Feb. 1981 -Jul. 1981 Yes Yes No
Oct. 1981 - Apr. 1982 Yes Yes Yes 1981:10:05 & 1982:02:2
Jun. 1982- Jul. 1982 No Yes Yes 1982:06:1
Mar. 1983 -May 1983 Yes Yes No 1983:03:21
Sept. 1983- Nov. 1983 No Yes No
Mar. 1984 - Sept. 1984 No Yes No
Dec. 1985 -May 1986 No Yes No 1986:04:0
Jan. 1987 No Yes No 1987:01:1
Mar. 1989 No Yes No
Jun. 1990 - Jul. 1990 No Yes No
Sept. 1992 - Dec. 1992 No Yes Yes
Aug. 1993 Yes Yes N/A* widening of ERM ban
* Period not covered by this measure
Table A.2.
Speculative Attack Episodes: Denmark
Dates of Attacks identified Attacks identified Attacks identified Realignment
Speculative Attacks by the exchange rate by the VAR by ERW's index Dates
switching model switching model of speculative pressure _
Jun. 1979- Dec. 1979 Yes Yes Yes 1979:09:24 & 1979:11:3
Jul. 1980-Mar. 1981 Yes Yes Yes
Oct. 1981 - Mar 1982 Yes Yes No 1981:10:05 & 1982:02:2
Jun. 1982 - Sep. 1982 Yes Yes No 1982:06:1
Mar. 1983 -May 1983 Yes Yes No 1983:03:21
Apr. 1986 No No No 1986:04:0
Jan. 1987 No No Yes 1987:01:1
Sept. 1992 No Yes Yes
Jul. 1993 - Aug. 1993 Yes Yes N/A* widening of ERM ban
* Period not covered by this measure
Table A.3.
Speculative Attack Episodes: France
Dates of Attacks identified Attacks identified Attacks identified Realignment
Speculative Attacks by the exchange rate by the VAR by ERWs index Dates
switching model switching model of speculative pressure
Sept. 1979 No No No 1979:09:24
Mar. 1981 -Nov. 1981 Yes Yes Yes 1981:10:05
Mar. 1982 - Sept. 1982 Yes Yes Yes 1982:06:14
Mar. 1983 - Apr. 1983 Yes Yes No 1983:03:21
Apr. 1986 - Aug. 1986 Yes Yes No 1986:04:07
Jan. 1987 Yes Yes Yes 1987:01:12
Nov. 1987 Yes No No
Sept. 1992 - Dec. 1992 No Yes Yes
Jul. 1993 -Aug. 1993 Yes Yes N/A* widening of ERM banc
* Period not covered by this measure
Table A.4.
Speculative Attack Episodes: Ireland
Dates of Attacks identified Attacks identified Attacks identified Realignment
Speculative Attacks by the exchange rate by the VAR by ERW's index Dates
switching model switching model of speculative pressure
Sept. 1979 N/A* N/A No 1979:09:2
Oct. 1981 N/A* N/A* No 1981:10:05
Jun. 1982 N/A* N/A* Yes 1982:06:1
Mar. 1983 - Jun. 1983 No Yes Yes 1983:03:21
Jan. 1986 - Aug. 1986 Yes Yes Yes 1986:04:07 & 1986:08:0
Dec. 1986 - Feb. 1987 Yes Yes Yes 1987:01:1
Sept. 1992 - Jan. 1993 No Yes Yes
Feb. 1993 - May 1993 Yes Yes Yes 1993:02:01
Jul. 1993 - Aug. 1993 Yes Yesi N/A* widening of ERM ban
* Period not covered by this measure
Table A.5.
Speculative Attack Episodes: Italy
Dates of Attacks identified Attacks identified Attacks identified Realignment
Speculative Attacks by the exchange rate by the VAR by ERW's index Dates
switching model switching model of speculative pressure
Sept. 1979 - Jan. 1980 Yes Yes No 1979:09:24
Mar. 1981 - Apr. 1981 Yes Yes No 1981:0323
Sept. 1981 -Oct. 1981 Yes Yes Yes 1981:10:05
Mar. 1982 - Apr. 1982 Yes Yes Yes
Jun. 1982 No No No 1982:06:1
Oct. 1982 -Nov. 1982 Yes No No
Mar. 1983 - Apr. 1983 Yes Yes No 1983:03:21
Sept. 1983 - Oct. 1983 Yes No No
Feb. 1984 Yes No No
Dec. 1984 Yes No No
Mar. 1985 - Apr. 1985 Yes Yes No
Jul. 1985 - Aug. 1985 Yes Yes No 1985:07:2
Dec. 1985 Yes No No
Apr. 1986 No Yes No 1986:04:0
Jan. 1987- May 1987 Yes Yes Yes 1987:01:12
Nov. 1987 Yes Yes No
Dec. 1989 Yes Yes No 1990:01:0
Sept. 1990 Yes No No
Jul. 1992 - Sept. 1992 Yes Yes Yes Exit ERM 1992:09:1
Table A.6
Speculative Attack Episodes: Spain
Dates of Attacks identified Attacks identified Attacks identified Realignment
Speculative Attacks by the exchange rate by the VAR by ERW's index Dates
switching model switching model of speculative pressure
Oct. 1989 - Dec. 1989 Yes Yes No
Apr. 1990- May 1990 Yes Yes No
Jul. 1990 - Mar. 1991 Yes No No
Dec. 1991 - May 1992 Yes No No
Sept. 1992 - Oct. 1992 Yes Yes Yes 1992:09:13 & 1992:11:2
Mar. 1993 -Aug. 1993 Yes Yes N/A* 1993:05:13
4 Period not covered by this measure
Table A.7.
Speculative Attack Episodes: UK
Dates of Attacks identified Attacks identified Attacks identified Realignment
SpeculativeAttacks by the exchange rate by the VAR by ERW's index Dates
switching model switching model ofspeculative pressure
Mar. 1991 -Jun.1991 Yes Yes No
Mar. 1992 - Sept. 1992 Yes Yes Yes Exit ERM 1992:09:1
Table A.8 - Evidence of Speculative Pressure: Belgium
1979:09-1980:05
* A realignment of EMS currencies took place on September 24, 1979. The revaluation of the Deutsche mark (DM)
implied a 2 percent devaluation of the Belgian franc against this currency. Source: IMF Annual Report on Exchange
Arrangements and Restrictions (IMF AREAR), 1980.
1981:02-1981:07
* The Belgian franc breached its divergence threshold several times in February-March 1980 and again in February 1981
necessitating intervention and tighter monetary measures. Source: IMF AREAR, 1981.
1981:10-1982:03
* On October 5, the DM was revalued by 5.5 percent. This realignment implied a 5.5 percent devaluation of the Belgian
franc vis-a-vis the DM. Subsequent to the realignment, the Belgian franc needed continued support. Pressure on the Belgian
franc intensified in December. On February 22, 1982 a realignment of the grid took place. The adjustment resulted in a
devaluation of the Belgian franc and the Danish krone by 8.5 percent and 3 percent, respectively, against other currencies.
Source: IMF AEAR, 1982.
1982:06
* With effect from June 14, 1982, a general realignment took place. The revaluation of the DM implied an effective 4
percent devaluation of the Belgian franc against this currency. Source: IMF AEAR, 1982.
1983:03-1983:04
* The Bundesbank intervened to support the Belgian franc. Source: The Economist, March 12, 1983.
* The Belgian franc was devalued with respect to the DM, Netherlands guilder, and the Danish krone but revalued against
the French franc, Italian lira, and Irish punt. The devaluation of the Belgian franc against the DM was of 4 percent. Source:
IMF AEAR, 1984.
* The National Bank of Belgium reportedly spent up to 15 billion Belgian francs in support of the franc. With the franc on
the floor of the EMS grid, the Belgian government announced a surprise 2.5 percent increase in interest rates to 14 percent.
Source: Financial Times, March 22, 1983.
1983:09-1983:10
* The Belgian central bank raised its discount rate from 9 to 10 percent. The central bank spent 80 billion Belgian francs
since August in support of the currency. Source: Financial Times, November 24, 1983.
1984:03-1984:09
* The National Bank of Belgium raised interest rates and intervened heavily in support of the Belgian franc, given that it
fell to its floor vis-a-vis the DM. Source: The Economist, March 10, 1984.
1985:12-1986:07
* On April 5, 1986, the Financial Times reported that the Belgian franc and Italian lira could follow the French franc in a
devaluation given the speculative pressure against these currencies. Source: Financial Times, April 5, 1986.
* With effect from April 7, a realignment of the ERM central rates took place. The realignment implied a 2 percent
devaluation of the Belgian franc against the DM. Source: IMF AEAR, 1987.
1987:01
* Speculation forced France, Belgium, and Denmark to raise interest rates to defend their currencies and, also, seriously
depleted these countries' reserves. Source: Financial Times, January 10, 1987.
* On January 12, 1987, the Belgian franc was devalued by only I percent against the DM in a general realignment.
Source: IMF AEAR, 1988.
1989:03
* The National Bank of Belgium raised key interest rates hiking the discount rate by 0.5 points to 8.75 in response to
speculative pressure against the franc. Source: Reuters News, April 21, 1989.
1990:06-1990:07
* Belgian foreign currency reserves fell 6.82 billion francs to 233.18 billion francs in the five days to July 20. Source:
Reuters News, July 25, 1990.
1992:09-1992:12
* No evidence of a speculative attack. According to Reuters, the Belgian franc remained stable throughout this period.
Rates were cut and the Belgian National bank intervened in favor of the lira and the punt.
1993:08
* Belgium tightened monetary conditions to resist speculative attacks against the Belgian franc pushing its central rate up
to 7.5 percent from 6.7 percent. Source: Financial Times, July 23rd, 1993.
Table A.9 - Evidence of Speculative Pressure: Denmark
1979:06-1979:12
* A realignment of EMS currencies took place on September 24, 1979. The Danish krone was devalued by 5 percent.
Subsequently, as part of a program to strengthen Denmark's extemal position, the bilateral intervention limits in that
country were raised by 5 percent against the Danish krone, effective November 30, 1979. Between March 1979 and
December 1979, the Danish krone reached its lower divergence thresholds on several occasions. Source: IMF AEAR, 1980.
1980:08-1981:03
* With effect, March 23, 1981, Italy devalued the lira by 6 percent against the other currencies participating in the EMS.
No other exchange rate adjustment was made, but the system became subject to occasional strain and the Danish krone
came under attack. Source: IMF AEAR, 1981.
1981:10
* On October 5, the Deutsche mark and the Netherlands guilder were revalued by 5.5 percent. This realignment implied an
effective 5 percent devaluation of the krone against the DM. Source: IMF AEAR, 1982.
1982:02
* The Danish krone came under intense pressure in early 1982 with the result that on February 22, 1982 a realignment of
the grid took place. The adjustment resulted in a 3 percent devaluation of the Danish krone against other currencies,
respectively. Source: IMF AEAR, 1982.
1982:06
e Following renewed pressure within the EMS a new realignment was decided upon on June 12. This realignment implied
a 4 percent devaluation of the krone against the DM. Source: IMF AEAR, 1982
1983:03
* The Danish krone reached its floor against the DM on March 9, 1983. Source: Financial Times, March 9, 1983.
* The Bundesbank intervened in support of the Danish krone. Source: The Economist, March 12, 1983.
* Pressures on exchange rates within the EMS reappeared. A general realignment took place that implied a 3 percent
devaluation of the Danish krone with respect to the DM and the guilder. Source: IMF AEAR, 1984.
1986:04
* The Danish krone was devalued by 2 percent with respect to the DM and the guilder in a general realignment. Source:
IMF AEAR, 1986.
1987:01
* Speculation forced France, Belgium, and Denmark to raise interest rates to defend their currencies and seriously depleted
their reserves. Source: Financial Times, January 10, 1987.
* A realignment of the central rates with the EMS was implemented on January 12, resulting in an effective 3 percent
devaluation of the krone vis-a-vis the DM. Source: IMF AEAR, 1988.
1992:09
* The Danish krone, French franc, and Irish punt fell to their floors in the ERM, prompting central bank intervention, as
speculators moved on to new targets after pummeling the pound, lira, and peseta. Source: Financial Times, September 18,
1992.
* The Danish Central bank countered speculative pressure against the krone by tightening money market liquidity. Source:
Reuters News, September 25, 1992.
1993:08
* The Danish krone was subject to intense speculative pressure similar to that suffered by the French franc with authorities
forced to increase interest rates in its defense. Source: Financial Times, July 24, 1993.
Table A.10 - Evidence of Speculative Pressure: France
1981:03-1981:11
* The French franc came under pressure following the French presidential elections and the monetary authorities of some
EMS members intervened heavily in May to keep the French franc within the agreed margins. The French franc and the
Belgian franc came under pressure against the DM again in September. Finally on October 5, a realignment took place that
entailed an overall depreciation of 8.1 percent of the franc against the DM and the Netherlands guilder. Source: IMF
AEAR, 1982.
* France spent $ 1.3b defending the franc prior to the October realignment. Source: The Economist, October 10, 1981.
1982:03-1982:09
* Following renewed pressure within the EMS, especially against the French franc, a realignment was decided on June 14,
1982. This realignment implied an effective devaluation of the franc against the DM of almost 10 percent. Source: IMF
AEAR, 1982-83.
1983:03
* A realignment of the parity grid took place March 21. The French franc was devalued against the DM by almost 8
percent. Source: IMF AEAR, 1984
1986:04
*A realignment took place that meant an effective 6 percent devaluation of the franc vis-a-vis the DM. Source: IMF AEAR,
1986.
* The devaluation of the French franc was the first major realignment of the EMS since 1983. Speculative pressure had
been mounting since mid-March. Source: Financial Times, April 7,1986.
1986:09-1987:01
* France raised rates by I percent after intense pressure on the franc. Source: Financial Times, December 9, 1986.
* Renewed pressure on the French franc forced the Bank of France to raise its seven-day repurchase rate by half a
percentage to 8.25 percent. The franc fell to a rate against the DM close to its floor. Source: Financial Times, December 31,
1986.
* Speculation forced France, Belgium, and Denmark to raise interest rates to defend their currencies. Source: Financial
Times, January 10, 1987.
* A realignment of the central rates within the EMS was implemented on January 12. This resulted in an appreciation of the
DM, Netherlands guilder, Belgian franc, and Luxembourg franc. All other currencies remained unchanged. Source: IMF
AEAR, 1987
1992:09-1992:12
* Germany and France forged a united front to defend the French franc from speculative attacks. France pushed its short-
term rate sharply higher and both the German Bundesbank and the Bank of France intervened heavily in support of the
franc. The Bank of France raised its five to ten day repurchase rate from 10.5 percent to 13 percent triggering a sharp rise in
money market rates. Source: Financial Times, September 24, 1992.
* The speculation against the ERM in September was unprecedented. Bundesbank intervention in support of the lira,
sterling, and French franc reached 92 billion DMs. Source: Financial Times, November 16, 1992.
* The French franc was again under pressure following the 6 percent devaluation of the Spanish peseta and Portuguese
escudo on November 22. Source: Financial Times, December 12, 1992.
1993:07-1993:08
* Massive central bank intervention estimated at 15 billion DMs on the part of the Bundesbank alone failed to give the
franc more than fleeting support in the ERM. The Bank of France pushed up its 24 hour lending rate to 10 percent from
7.75 percent. Source: Financial Times, July 24, 1993.
Table A.11 - Evidence of Speculative Pressure: Ireland
1983:03-1983:06
A realignment of the parities took place on March 21. The punt was devalued by 8.53 percent against the DM. Source:
IMF AEAR, 1984.
1986:01-1986:08
* In April 1986, a realignment resulted in a devaluation of the punt of 3 percent against the DM. In August 1986, the Irish
punt was devalued again, this time by 8 percent. Source: IMF AEAR, 1987
1986:12-1987:01
a The Irish punt was effectively devalued by 3 percent against the DM.
1992:09-93:02
* The Danish krone, French franc, and Irish punt fell to their floors in the ERM, prompting central bank intervention, as
speculators moved on to new targets after pummeling the pound, lira, and peseta. Source: Financial Times, September 18,
1992.
* The Irish punt was under intense selling pressure during September 1992. Source: Financial Times, September 24, 1992.
* The Irish punt was unaffected by the November realignment of the peseta and the escudo despite of speculation against
this currency. Source: Financial Times, November 23, 1992.
* Ireland was forced to push interest rates to usurious levels in the hope of maintaining the exchange rate parities. Source:
Financial Times, December 12, 1992.
* On January 30, 1993, the Irish punt was devalued by 10 percent. Source: IMF AEAR, 1994.
1993:07-08
* The punt plunged nearly 3.5 percent in just three days following the recent ERM band widening. Source: Reuters News.
August 10, 1993.
Table A.12 - Evidence of Speculative Pressure: Italy
1979:09-1980:01
* On September 24, 1979 the lira was effectively devalued by 2 percent against the DM. Source: IMF AEAR, 1980.
1981:03
* The Italian lira came under pressure at the beginning of March. Effective March 23, the Italian lira was devalued by 6
percent against all other EMS currencies. Source: IMF AEAR, 1981.
1981:09-1981:10
* On October 5, the lira was effectively devalued by 8 percent against the DM. Source: IMF AEAR, 1982
1982:03
* Following the devaluation of the Belgian franc (8.5 percent) in February 1982, France and Italy came under attacks. With
inflation in France and Italy running at twice the Belgian level speculative attacks shifted towards these most vulnerable
members. Source: Financial Times, April 1, 1982.
1982:06
* In June 14, 1982, the lira was effectively devalued by 7 percent against the DM. Source: IMF AEAR, 1982.
1983:03
* Changes in the grid meant that the lira was devalued by 8 percent against the DM. Source: IMF AEAR, 1984.
1985:03-198:04
No evidence.
1985:07
* The central rates of the EMS were realigned. Changes involved a devaluation of 7.8 percent in the bilateral central rates
of the Italian lira. Source: IMF AEAR, 1986
1986:04
* Italian lira and French franc were devalued against all other currencies.
The realignment implied an effective 3 percent devaluation of the lira. Source: IMF AEAR, 1987
87:11
No evidence.
1989:12
* The Bank of Italy sold 161 million marks at the fixed rate as the lira slid to its 15 month low against the mark. Source:
Reuters news, December 15, 1989.
* The lira was devalued by 3.8 percent against its EMS central rates on January 10, 1990. Source: Financial Times, January
10, 1990.
1992:09
* Italy forced up interest rates by 1.75 percent points, the biggest increase in 11 years and drew on international bank
credits in an attempt to protect the lira. Source: Financial Times, September 6, 1992.
* On September 13, the central rate of the lira against other currencies was devalued by 7 percent.
* On September 17, the lira was withdrawn from the ERM. Source: IMF AEAR, 1993.
Table A.13 - Evidence of Speculative Pressure: Spain
1989:10-1989:12
* October 24, Spain fights pressure against the peseta. Source: Financial Times, October 24, 1989.
* October 26, The Bank of Spain intervened to support the peseta selling 290 million marks at a fixing rate of 63.8 pesetas.
It was the central bank fifth intervention in a week. Source: Reuters news October 26, 1989.
* Bank of Spain sold 96.65 million dollars to brake the Spanish currency decline against the mark. Source: Reuters news
October 20, 1989.
1990:04-1990:06
No evidence
1992:09-1992:11
* On September 17, the central rate of the peseta against the central rates of the currencies participating in the ERM of the
EMS was devalued by 4.8 percent. Source: IMF AEAR, 1993.
* On November 22, the central rate of the peseta against the central rates of the currencies participating in the ERM of the
EMS was devalued by 6 percent. Source: IMF AEAR, 1993.
1993:04-1993:08
* The Bank of Spain raised unofficial intervention rates and intervened strongly on the market to defend the peseta's central
parity rate. The Madrid authorities took action by raising intervention rates from 13.35 percent to 14 percent and put aside
45 billion dollars for the peseta's defense. Source: Financial Times, April 23, 1993.
* On May 13, the central rate of the peseta against the central rates of the currencies participating in the ERM of the EMS
was devalued by 8 percent. Source: IMF AEAR, 1994.
Table A.14 - Evidence of Speculative Pressure: UK
1991:04-1991:06
No evidence
1992:03-1992:09
* The British government borrowed 7.27 billion pounds of DM and other currencies and sold these for sterling to support
the currency. Source: Financial Times, September 4, 1992.
* On September 16, the UK suspended intervention obligations with respect to the exchange and intervention mechanism
of the EMS. Source: IMF AEAR, 1993.
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