Central Bank Review ISSN 1303-0701 print / 1305-8800 online
© 2006 Central Bank of the Republic of Turkey
http://www.tcmb.gov.tr/research/review/
Assessing the Currency Crises in Turkey
Elif Çepni+ and Nezir Köse++
+
Dogus University, Acıbadem, Zeamet sok. No: 21
34722 Kadıköy/İstanbul
ecepni@dogus.edu.tr
Gazi University, İ.İ.B.F.
06500 Beşevler/Ankara
nezir@gazi.edu.tr
++
Abstract
This study presents the significance of the currency crises, discusses the related
literature and applies a model of economic vulnerability to Turkey during 1985Q2-2004Q2.
The common approach in currency crisis literature is to focus on the performance of
thresholds for a set of early warning indicators. Following the explanation of “Index of
Speculative Pressure” (ISP), Granger causes of the ISP is discussed. The study shows that,
current account/ GDP ratio, M2/international reserves ratio, real credit growth and current
account/foreign direct investment ratio are Granger causes of the ISP at 1 % level. Then by
using Vector Auto Regression (VAR) model, the ISP index is forecasted. The study shows
that the combination of VAR(1)+VAR(2)+VAR(5) models generate relatively better forecast
values than all other single models. Finally the study estimates dynamic probit and logit
models by using maximum likelihood to predict currency crises. It shows that logit model
gives a better performance than the probit, for a better prediction of the probabilities of the
Turkish currency crises. The most important contribution of this study is to show that the
logit model has a very high performance in the prediction of Turkish currency crises. It can
be used to foresee forthcoming currency crises. Also the forecast of the ISP (as a level) is
giving very successful results. It is observed that the ISP and forecasted ISP values are
almost moving together or very close to each other.
JEL Classification: C25, E44, F3, F47.
Keywords: Currency Crises, Speculative Pressure, Exchange Rates, Financial Crises.
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1. Introduction
The volatility of exchange rates around the world has tended to grow with the
massive growth in “hot money” since the early 1970s. There are many reasons of
this volatility including; a growth in international financial markets encouraging the
international transfer of money, a liberalisation of international financial
movements combined with easier computer transfer funds, a massive growth in
speculative activities of trading companies, banks and other financial institutions,
the growing belief that rumour and “jumping on the bandwagon” are more
important determinants of currency buying or selling than cool long-term appraisal.
Today it has become increasingly difficult for countries to counteract speculation
on their own. The scale of foreign exchange markets movements makes any
significant speculation too great for individual countries to resist.
Notwithstanding most governments and firms dislike highly volatile exchange
rates, most of them insisting in staying flexible exchange rate regime. Turkey is not
an exception. During 1990s, economic crisis started to affect the Turkish economy
with increasing frequency. After the adoption of Structural Adjustment and
Stabilization Program (which is called 24 January 1980 Decisions), Turkey
liberalized its economy to integrate with the world economy. In line with this goal,
a lot of new laws were passed to liberalize foreign trade and financial movements.
Decree no.32 on the protection of the value of the Turkish currency which went into
effect on 11 August 1989 has formed the legal framework necessary for the
transformation of the Turkish currency to “convertibility (Çepni, 2003).
With this decree, the liberalization of the foreign exchange regime and capital
movements was to a large extent completed. After switching to a free floating
regime, Turkey started to encounter with more frequent crises. While external
factors played a significant role (1991 Gulf crises, 1999 earthquakes etc), the main
reasons of these crises were: the development of an unsustainable domestic debt
dynamic and unhealthy structure of the financial sector, with particular problems
caused by the state banks and by the failure of structural problems.
At the end of 1999, Turkey embarked upon an ambitious Stabilization
programme, aimed at achieving single digit inflation by 2002. A nominal anchor
was set for reducing inflation expectations, sounder public finance and wideranging structural reforms designed to liberalize and modernize the economy.
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39
Significant progress was made during 2000. But a severe banking crisis blew up in
late November, accompanied a massive capital outflow.
The challenge for banking reform is particularly arising from “unlimited Turkish
lira-deposit insurance” introduced after the 1994 crisis. This insurance is the main
source of banking system moral hazard (Policy Reassessment, 2001).
Notwithstanding the economic programme adopted at the beginning of 2000
achieved a lot of pre-determined goals of the economic policy, through the end of
the year it caused growing concerns about he sustainability of the exchange rate
regime.
On 21 November 2000, there was more than 1.4 billion $ demand for foreign
currency, therefore, the Central Bank stopped providing liquidity and the overnight
interest rate (simple annual) reached its peak of 800 percent on December 4, 2000.
Political uncertainties and the cautious approach of international capital towards
emerging markets as a result of developments in Argentina led to a decrease in
capital inflow to Turkey especially in the second half of 2000.
The capital outflow only halted and devaluation fears allayed by the
announcement on 6 December of a large IMF package planning to give $7.5 billion
additional loan from the Supplemental Reserve Facility, (SRF) in addition to $5
billion from the World Bank.
On February 19th 2001, Bülent Ecevit, the prime minister was accused by Ahmet
Necdet Sezer, the president for his half-hearted pursuit of corrupt politicians and not
doing enough to fight corruption at National Security Council meeting. There was a
scheduled domestic debt auction of the Treasury on February 20, 2001, the day
before the maturing of $5 billion domestic dept. the auction aimed at borrowing
approximately $ 5 billion which was almost 10 percent of the domestic debt.
This untimely row caused much more serious crisis than the November 2000
crisis. The markets took the news badly, fearing that infighting might topple the
government and these developments brought Turkey’s much-needed economic and
political overhaul to an untimely end. Jittery investors started pulling billions out of
the country, seriously denting the Central Bank’s reserves of foreign exchange.
On the 19th of February the Central Bank’s reserves declined by $5.1 billion
dollars within 2 hours. The stock market (ISE National 100) lost 18% of its value
on the 21st of February ended the day at 7180.
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On the 22nd of February the repo interest rates were achieved to 7500% which
gave the average 4400% for the mentioned day. In order to obstruct this climb in
the interest rates, the Central Bank sold money at 1000% interest rate level. Nothing
was enough to restore the confidence in the market. With every passing day, the
uncertainty and the degree of the loss in the confidence started to increase. In the
end, the government had no choice but the abandon the lira’s “crawling peg”.
The Turkish exchange rate system was a “crawling peg” which allowed the
external value of the lira by no more than 15 % against a currency basket
comprising the dollar and euro a year. The Turkish government was not the only
looser when the country’s currency collapsed on the 22nd of February. The IMF
which helped design the Turkish exchange rate regime as a part of an $11.5 billion
lending programme was accused for writing wrong prescriptions (Turkey in
Turmoil, 2001).
There are many countries that are facing with similar crises on the world.
Because of such dangers, currency crises have been the subject of an extensive
economic literature, both theoretical and empirical. Still there exist a lot of unsolved
issues; each new set of crises presents new puzzles.
An exchange rate regime remains nearly as controversial as it was at the outset.
Sharply different regimes continue to coexist, from currency boards to relatively
free floating. Exchange rate policy lies at the nexus of all strategic policy choices
(Begg et al., 1999, 1).
In most countries exchange rate policy was dominated by the trade off between
disinflation and external competitiveness.
At a casual glance, the IMF’s attitude towards exchange rates seems
extraordinarily erratic. In 1997 the Fund urged Asian countries to devalue or float
their currencies. In 1998 it lent billions to Russia and Brazil to try to help them
maintain their exchange rates. It has praised Hong Kong for its super-strict currency
board, and feted Singapore for its flexible managed float. Given that exchange-rate
regimes are by definition central to currency crises, such different approaches
cannot all be ideal (global finance, fix or float, 1999).
It is one area where the trade-offs cannot be fudged. In a world of increasingly
mobile capital, countries cannot fix their exchange rate and at the same time
maintain an independent monetary policy. They must choose between the
confidence and stability provided by a fixed exchange rate and the control over
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41
policy offered by a floating rate. Traditionally, the deciding factor in a country’s
choice has been its vulnerability to external shocks, such as sudden shifts in
commodity prices. A floating currency allows a country to adjust to external shocks
through the exchange rate. In countries with a fixed currency, domestic wages and
prices will come under pressure instead.
Most academics now believe that only radical solutions will work: either
currencies must float freely, or they must be tightly tied (through a currency board
or, even better, currency union). Unfortunately the academics rarely agree on the
best solution.
Policymakers prefer to play down exchange rates. Any regime can work, they
argue, provided it is backed by sound economic fundamentals. That is true but trite.
Of course a country will benefit from sound fiscal and monetary policies; but, as
recent events have shown, a country’s choice of exchange-rate regime clearly
affects its vulnerability to crises. Asian countries got into trouble because of their
exchange-rate pegs, and were then thrown into chaos by the volatility of floating
rates.
Different countries will have taken different routes to achieving the “impossible
trinity” of integration, regulation and sovereignty. Those in regional unions will
have given up sovereignty for integration; those with floating rates will have
maintained sovereignty, but often at the cost of restricting integration with the rest
of the world (global finance, fix or float, 1999).
The international monetary and financial system has evolved incrementally from
the gold standard to the gold-exchange standard, to the Bretton Woods gold-dollar
system, and now to the post-Brettton Woods “nonsystem”.
With the outbreak of the Asian crisis, a new urgency came to be attached to
efforts to reform and strengthen the international financial system. The international
system is a dense network of social, economic, and financial institutions. As with
any complex mechanism, there are limits on the feasible changes to any one
component so long as the others remain in place.
The prevailing system is widely criticized but it is not discredited. To achieve
such a consensus is immensely complicated by the number of governments and
interest groups involved. In contrast to the situation during the Second World War,
the world today is a more multipolar place. There is the G-3, the G-7, the G-10, the
G-20, and a host of other (Eichengreen, 2002, 3).
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Financial markets are markets in information, and information by its nature is
asymmetric and incomplete. Sharp changes in asset prices, sometimes so sharp as to
threaten the stability of the financial system and the economy will occur from time
to time. It follows that crises have always will be and that they remain a particular
problem in developing countries. Bordo et al. (2001) shows that currency, banking
and twin crises are hardly perennials as well (twin crises are when currency crises
and banking crises come together, a twin crises is said to occur when there are
currency and banking crises in the same or immediately adjoining years)
(Eichengreen, 2002, p.5).
In this paper, after the presentation of the significance of the topic (currency
crises) in this introduction part, related literature on the theory of currency crisis
will be discussed in the forthcoming part and finally a model of economic
vulnerability will be applied to Turkey in the last part. The common approach in
currency crisis literature is to focus on the performance of thresholds for a set of
early warning indicators.
2. Theory
Currency crisis literature comprises different types of analysis. But these
analyses can be categorized under two main headings. The first category focuses on
“the prevention of the crisis” whereas the second one concentrates on “how to
manage a crisis once it occurs”.
Studies that can be put into the first category try to find an answer for “how
crises can be prevented or what can be done to avoid or minimize crises” and
studies that can be put into the second category try to find an answer for “if it
occurs, what is the best policy response that must be given to resolve and manage
crisis”.
It can be said that a number of useful steps have been taken on prevention side
but little has been achieved in terms of how to manage and resolve crises
(Eichengreen, 2002).
Studies on the prevention side can be categorized under different titles as well.
Our categorization is based on the studies presented at a National Bureau Economic
Research Conference held in 2001.
The first category consists of studies on “the role of the current account and trade
flows in financial crises”. Whether large deficits increase the probability of a
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43
currency crisis or not, are trade linkages important determinants of country
vulnerability to crises (this is also called the contagion effect showing the
transmission of currency crises across countries) or not and similar questions are
tried to be answered.
The second category consists studies on the role of financial players (including
banks, large hedge funds, private sector investors and speculators). Whether the
exchange and interest rate polices in the advanced countries affect capital flows to
emerging countries, whether the presence of large agents increases a country’s
vulnerability to a crisis and similar questions arise in these studies. Some policy
makers and analysts express their concern that the activity of large players in small
markets (big elephants in small ponds) may trigger crises that are not justified by
fundamentals, destabilizing foreign exchange and other asset markets, creating
systemic risk, and threatening the stability of the international financial system
(Corsetti et al., 2001).
The third category includes studies concentrating on the role of financial
liberalization. The importance of capital controls and whether capital controls are
working or not analysed by many studies. It has been argued that unrestricted
capital mobility was at the centre of global financial instability. It is believed that
speculators focus exclusively on the short run (also speculators are often affected by
rumours) and tend to flee countries at the first signs of trouble. Many economists
believe that restricting capital mobility can reduce the frequency and depth of
financial crises.
The fourth categorization focuses on the role of capital flows. The balance sheet
effects and crony capitalism are the main points in these studies. In a world with
increased capital mobility, currency crises have very important balance sheet
effects. If the corporate sector has significant liabilities expressed in foreign
currency, devaluation can generate massive bankruptcies. Most probably this will
cause an increase in local banks’ nonperforming loans. Moreover if the extent of
“crony capitalism” is big in a country the negative effects will be more severe. If
banks lend to friends and “cronies” and tend to inflate the value of collateral, in the
downturn they cannot recover easily. Empirical evidence shows that banks’
difficulties can grow quickly and become very costly to clean up. Restructuring the
corporate and the banking sectors could cost a lot for the country.
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The traditional models of currency crises follow the seminal work by Paul
Krugman (1979), and Maurice Obstfeld (1986). The alternative view on currency
crises stems from the writings of Hyman Minsky (1982, 1977,1975) championed by
Charles Kindleberger (1996) in his classic work Manias, Panics and Crashes.
Krugman’s work has been simplified and extended by several authors including
Flood and Garber (1984), Connolly and Taylor (1984), Calvo (1987), Krugman and
Rotemberg (1991), Flood, Garber and Kramer (1996) (Saqib, 2002).
Generally three varieties of financial crisis are mentioned in literature. The first
one is the early literature describing crises triggered by unsustainable policies. The
model first laid out by Krugman in 1979 and refined by Flood and Garber in 1984.
Krugman model (1979) is an example of a “first generation” speculative attack
model. Alternatively such models are sometimes called “canonical currency crisis
models”.
The canonical currency crisis model explains such crises as a result of a
fundamental inconsistency between domestic policies, typically the persistence of
money-finance budget deficits and the attempt to maintain a fixed exchange rate. In
these models, the breakdown of the fixed exchange rate system is inevitable
because the authorities are attempting to pursue two policies which are not
compatible in the long-run, the maintenance of a fixed exchange rate and the
creation of domestic credit at a faster rate than that compatible with maintaining
fixed exchange rate (Fane, 2000, 87). Standard example is the expansionary
monetary policy leading to inflation and creating pressure on the exchange rate
(devaluation) which the central bank can only resist to a certain extent.
The inconsistency can be temporarily papered over if the central bank has
sufficiently large reserves, but when these reserves become inadequate speculators
force the issue with a wave of selling (Krugman, 1997).
The theory of first generation models provides some useful insights to
understand the balance of payments crisis. In the first place is the identification of
the relevant macroeconomic fundamentals whose variation in a certain trend helps
to foresee the crisis. Related to this is its demonstration of speculative attacks that
are fully anticipated, as opposed to irrational panics. Secondly, given particular
values of fundamentals, timing of the crisis can be fixed. Finally, crisis is
unavoidable. In other words, first generation models are subject to unique
equilibrium (Saqib, 2002).
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45
Fundamental based crises can be predicted. The usual signals were flashing:
large and growing budget deficits, matched by large and growing current account
deficits. For this kind of crisis, early warning signals can work but they can be
fairly trivial and readily missed.
Following Flood and Garber (1984) and Obstfeld (1986), the second generation
models note that the crises in foreign exchange market are due to rational and selffulfilling expectations, which can be triggered by random events. Speculative
attacks may be self fulfilling, in the sense that multiple equilibriums are possible,
any one of which can be sustained indefinitely provided that everyone believes that
it will be sustained (Fane, 2000, 91). Currency crises can occur without necessarily
being caused by fundamental factors. Even currencies that are stable can be targets
of a speculative attack.
There is a “good” equilibrium where markets do not attack the currency and the
authorities’ preference is to maintain the peg, which is possible since the
fundamentals allow the survival of the regime. Simultaneously there exists a “bad”
equilibrium where an attack, if it were to occur, would succeed.
There may exist several (or an infinity of) “bad” equilibriums corresponding to
various sizes of the post crisis depreciation. The cause of multiple equilibria is that;
markets act on the basis of expectations of a particular outcome. What makes a
crisis occur is the belief that it can occur. Expectations that are ex ante unjustified
are validated ex post by the outcome that they have provoked.
The EMS (European Monetary System) crisis of 1992-1993, the Mexico crisis of
1994-1995 and the Asian crisis (Thailand excepted) all exhibit features compatible
with the assumption of self fulfilling attacks (The Exchange Rate: Threats, 1999,
p.61).
There are many possible ways in which multiple equilibriums can arise. For
example an expectation of devaluation may be self fulfilling if it results in money
wages being set at higher levels than those that would be set if workers and
employers were confident that the exchange rate would not be devalued. The reason
is that if money wages have been set at relatively high levels, the authorities are
under more pressure to reduce unemployment by devaluing than if wages had been
set at lower levels. In this example “herding” behaviour is in the self interest of
employers and employees (Fane, 2000, 92).
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What sets self fulfilling crises off is a difficult question to answer. Anything
could in principle be the trigger. That is “sunspot” dynamics, in which any arbitrary
piece of information becomes relevant if market participants believe it is relevant.
Both the canonical currency crisis model and the second generation models
presume that foreign exchange markets are efficient, that they make the best use of
the available information. What difference might inefficient markets make to the
study of currency crises? The most obvious difference is the possibility of
“herding”. In the context of a currency crisis, this means that a wave of selling,
whatever its initial cause, could be magnified through sheer imitation and turn quite
literally, into a stampede out of the currency.
According to theorists herding might be arising from “bandwagon effects” driven
by the awareness that investors have private information or might be arising from
“principal-agent” problem. Much of the money that has been invested in crisisprone countries is managed by agents rather than directly by principals.
Kehoe and Chari (1996) have argued that bandwagon effects and markets with
private information create a sort of “hot money” that at least sometimes causes
foreign exchange markets to overreact to news about national economic prospects.
Related with principal-agent problem, we can imagine a pension fund manager
investing in emerging market funds. She will have far more to lose from staying in
a currently unpopular market and turning out to be wrong than she does gain from
sticking with the market and turning out to be right. To the extent that money
managers are compensated based on comparison with other money managers, then
may have strong incentives to act alike even if they have information suggesting
that the market’s judgement is in fact wrong (Krugman, 1997).
The Asian crisis led to a proliferation of “third generation” models, quite
different from the first or the second generation because in the major crisis
countries of Asia, neither of these stories seems to have much relevance. These
attempts caused the introduction of the third generation models. In these models the
core of the problem lies in the banking system (Krugman, 1999). Third generation
crises are financial factors dominated crises.
There are three main variants of the third generation crises. One version involves
moral-hazard driven investment, which leads to an excessive build up of external
debt and then to a collapse. It implies that there should be over-investment and
excessive risk-taking by entrepreneurs with access to guaranteed finance, but also
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47
that the availability of implicit guarantees should tend to crowd out legitimate
investment that bears the full burden of risk.
A second version put an emphasis especially to financial fragility. These are
bank run centred models (open economy version). The second version, largely
associated with the 1998 study of Chang and Velasco, is built around openeconomy versions of the Diamaond-Dybving bank-run model. In this model,
investors face a choice between short-term investments with a low rate of return and
long-run investments with a higher rate of return. Unfortunately, the long run
investments yield relatively little if they must be liquidated prematurely, and
investors are assumed to be unsure ex ante about when they will want to consume.
Financial intermediaries can resolve this dilemma by pooling the resources of many
investors and relying on the law of large numbers to avoid holding more short-term
assets than necessary. However, such intermediaries then become vulnerable to
self-fulfilling panics, in which fear of losses leads depositors to demand immediate
payment, forcing destructive liquidation of long-run assets that validates these fears
(Krugman, 1999). But some economists argue that the bulk of the bad loan problem
is a consequence of the crisis, of the recessions and currency depreciations that
followed the collapse of capital inflows.
The third version of the third generation models stresses the balance-sheet
implications of currency depreciation. These models attempt to combine moral
hazard driven bubble with a balance sheet driven crisis when the bubble burst. The
deterioration of balance sheets played key role in the Asian crisis. The explosion in
the domestic currency value of balance sheets is having a disastrous effect on firms.
The prospects for recovery are generally very difficult because of the weak
financial condition of firms. Their capital is wiped out by the combination of
declining sales, high interest rates, and a depreciated currency. These balance sheet
problems are in turn a cause of the problem of non-performing loans at the banks,
they are not a banking problem per se, and even a recapitalization of the banks
would still leave the problem of financially weakened companies untouched.
To explain financial crises, apart from first, second and third generation models,
there is another model called Kindleberger Minsky model in the literature of
currency crises.
Kindleberger (1996) describes three phases of a process that leads to a financial
crisis namely mania, panic and crash. Manias take place at the same time of
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business cycle expansion when economic agents change from liquid to real or
financial assets. Panics are characterised by stampede, race for changing real or
financial assets into money. Crash is the final outcome of the process preceded by
panic and mania.
Compared to these phases, a model is summarised in five different stages which
are, displacement, boom, overtrading, revulsion and tranquillity.
The crisis starts out with an exogenous shock, significantly large and pervasive,
to the macroeconomic system. Minsky calls it “displacement”. The source of
displacement can be an invention, political event, war, crop failure, policy change
and etc. Displacement alters profit opportunities in at least one sector of the
economy. This sector could be new or already in existence.
In the boom phase, money supply enlarges through the expansion of bank credit.
The third stage is called “overtrading” and refers to the process of ever increasing
investment and income. Overtrading involves: speculation, buying for resale rather
than use or income. As individuals and firms see others making profit, they tend to
join the trend.
The fourth stage is called “revulsion”. As the boom continues, interest rates,
prices, profits, velocity of circulation, all continue to increase. Gradually or
suddenly with the persistence of distress, speculators realise that the market cannot
go higher. The crisis looms. This realisation may turn into a stampede, race of
liquidation. Bankruptcies, insolvent banks, unearthing of a fraud or a swindler are
some specific signals. The final stage is called “tranquillity”. The panic continues to
feed itself, until the market realises that sufficient money will be available to meet
the demand for cash (despite the fact that panic cannot go on forever) (Saqib, 2002,
p.12).
Although all above mentioned models have made serious contributions to the
literature, all have some deficiencies. There is no hard and fast rule about the timing
of crises. It is surprising how long basically unsustainable situations can endure,
notably if an election is in sight. It is clear that the more the crisis is postponed, the
worse the balance sheet and the larger the fallout, once it does happen.
3. Measuring Financial Crises
During 1995-2001, over a dozen emerging market experienced severe financial
crises. Numerous empirical studies seek to identify causes for past crises and early-
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49
warning indicators that might be used to avoid crises. What has changed in the
economic environment, are these crises different from earlier crises, what are the
indicators of vulnerability which could be used as leading indicators of crises, is it
possible to predict crises and many more questions arose and are tried to be
answered by many studies.
Goldstein (1996), Kaminsky et al. (1997), and Goldstein and Reinhart (1998) are
at the forefront of this effort, with several other papers.
The presumption behind this research effort is that crises should be foreseen.
Stemming from the predictions of first, second and third generation models, a fairly
large number of empirical studies on the determinants of currency crisis have
emerged. These studies can be classified under two categories. First category
investigates the determinants of crises in a single country analysis. While the
second focuses on multi-country analysis. Generally the country specific studies
suggest that domestic macroeconomic indicators play a key role in undermining an
exchange rate peg.
Domestic credit growth, exchange rate misalignments, foreign exchange reserve
losses, debt structure, expansionary fiscal and monetary policies are some of the
leading indicators suggested by these studies.
The definition of the crisis changes from study to study. Table 2 (single country
literature) and Table 3 (multiple country literature) in the annexes summarize the
studies showing indicators of currency crises in single country literature and in
multiple country literature.
If crises are first generation or second generation, we can use theory and past
experience to identify these weaknesses that make attacks possible and attempt to
estimate models to be used for forecasting.
Since these weaknesses are necessary but not sufficient conditions for a crisis to
occur, the forecasting properties of such models can be misleading. If we use the
terminology of statistical tests, leading crisis indicators face type I errors when they
fail to predict attacks that occur and type II errors when they predict attacks that do
not occur.
There are weaknesses of current methods. Today various methodologies and
variables are used to characterize the period preceding currency crises and to assess
the probability of such crises.
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Generally the classification of the main indicators by category used in empirical
works is as follows:
Current account: real exchange rate, current account balance, trade balance,
exports, imports, terms of trade, price of exports, savings, and investment.
International variables: foreign real GDP growth, interest rates, and price level.
Financial liberalization: credit growth, change in the money multiplier, real
interest rates, and spread between bank lending and deposit interest rates.
Other financial variables: central bank credit to the banking system, gap between
money demand and supply, money growth, bond yields, domestic inflation,
"shadow" exchange rate, parallel market exchange rate premium, central exchange
rate parity, position of the exchange rate within the official band, and
M2/international reserves.
Real sector: real GDP growth, output, output gap, employment/unemployment,
wages, and changes in stock prices.
Fiscal variables: fiscal deficit, government consumption, and credit to the public
sector.
Institutional/structural factors: openness, trade concentration, dummies for
multiple exchange rates, exchange controls, duration of the fixed exchange rate
periods, financial liberalization, banking crises, past foreign exchange market
crises, and past foreign exchange market events.
Political variables: dummies for elections, incumbent electoral victory or loss,
change of government, legal executive transfer, illegal executive transfer, left-wing
government, and new finance minister; also, degree of political instability
(qualitative variable based on judgment) (Kaminsky et al., 1998).
Signal approach monitors the evolution of a number of economic variables.
When one of these variables deviates from its normal level beyond a certain
“threshold” value, this is taken as a warning signal about a possible currency crisis
within a specified time period time.
4. Data and Empirical Results
The list of the variables used in this study will be as follows:
1. The Weighted Index of Speculative Pressure (ISP): In the currency crisis
literature (especially by following Kaminsky, Lizondo and Reinhart (1998)) Index
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of Speculative Pressure (ISP) is used to define currency crisis. The same way will
be followed in this study as well.
ISP is a weighted average of three variables namely, quarterly percentage change
in exchange rate(e), percentage change of a headline interest rate (r) , quarterly
percentage change of international reserves (ir). (ISP= w1.e + w2.r – w3..ir).
(Kroska, 2000).
Eichengreen et al. showed that different weights do not have significant impact
on the empirical results and to avoid a necessity to estimate weights which, ideally,
would change over time, the weights are generally standardised (w1=w2 =w3 = 1) in
some studies.. Alternatively in some models weights are taken as inversely
proportional to the standard deviation of each of the three variables. A period of
excessive market volatility is defined as the period during which the index rises
above a pre-specified threshold based on the previous n observations.
The index captures either a successful attack (a sharp devaluation), or a
successful defence (the exchange rate remains unchanged but the monetary
authorities deter an attack by a combination of interest rate increases and foreign
market interventions), or an unsuccessful defence (all three variables move
sharply).
In order to find a probability of crisis, the threshold levels must be determined. If
index value exceeds the threshold level, a crisis signal is issued.
In some empirical applications, periods in which the index is above its mean by
more than three standard deviations are defined as crises. In some of them crises
defined with 2 standard errors. When the threshold is lowered, the number of
warning signals misses real crises but detects milder events. If we decrease бn the
size of the deviation of index from its sample mean, more foreign exchange market
tensions could be labelled as crises.
ISP = 1 * ∆ e + 1 * ∆ r − 1 * ∆ ir
σ
e
e
t −1
σ
r
r
t −1
σ
ir
ir
t −1
σ e is the standard deviation of the exchange rate, σ r is the standard deviation of the
interest rate and σ ir is the standard deviation of the international reserves.
If the ISP ≥ µn + t(n,q). бn than this is taken as a crisis. Where µn is the sample
mean and бn is the sample standard deviation of the ISP based on the previous n
observation (Kroska, 2000).
52
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
In this study, periods in which the index is above its mean by more than two
standard deviations are defined as crises. The weighted ISP values can be found in
Table 1 of the appendix.
2. Real Exchange Rate (RER): (CPI of USA/ CPI of Turkey) (Nominal
Exchange rate).
Data is obtained from “International Financial Statistics” CD-ROM produced by
the IMF.
3. Industrial Production Index of Turkey (INDTR): Seasonally adjusted
industrial production index of Turkey whose base year is 1995 (1995=100) was
obtained from IFS CD-ROM of the IMF.
4. Foreign Direct Investment as a ratio of the Gross Domestic Product
(FDIGDP): Foreign Direct Investment shows the inflow to Turkey not reflects the
“net” value and it is expressed as (FDI/GDP)*100.
5. Current Account as percentage of GDP (CAGDP): Data is obtained from the
electronic data delivery system of the Central bank of Turkey (CBRT).
6. M2 as a ratio of international reserves (M2IR): Data was obtained from the
CD-ROM of International Statistics of the IMF.
7. Terms of Trade (TT): It is calculated as “exports price index/imports price
index” and data was obtained from the electronic data delivery system of the
CBRT.
8. Industrial Production Index of the European Union (15 Countries) (INDEU):
Seasonally adjusted industrial production index whose base year is 1995
(1995=100) was obtained from Eurostat.
9. Real Credit Growth (RCG): First the value of credits (public+private) was
found and than by using Consumer Price Index of 1987 the nominal values of
credits were corrected for the inflation. RCG= Total Credits/CPI (1987).
10. Foreign Direct Investment +Current Account (FDICA): Data obtained from
the electronic data delivery system of the CBRT.
11. Trade Balance (TB): (Export-Import): Data was obtained form the CD-ROM
of International Statistics of the IMF.
53
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
5. The Granger Causes of the Index of Speculative Pressure
A better term for Granger causality is precedence. Therefore, this test can be
used for determining preceding indicators of ISP. We may also use the results of
Granger causality for evaluating forecasting performance of ISP since it is
concerned with one-ahead forecast accuracy.
Sims, Stock and Watson (1990) have shown that nonstandard distribution must
be applied for sequential testing procedure if the variables are nonstationary.
Therefore, Granger causality test is valid only approximately or not be valid at all
for nonstationary variables. To overcome this problem, Toda and Yamamoto (1995)
proposed an alternative approach for testing coefficient restrictions of a level
(possibly nonstationary) model. Their procedure considers a lag augmented or
modified Wald (M-Wald) test which has conventional asymptotic chi-square (χ²)
distribution when a VAR (p+dmax) is estimated where dmax is the maximal order of
integration suspected to occur in the system. In other words, this lag augmentation
procedure provides standard asymptotic although the time series have
integration/cointegration properties, and therefore, can be applied without a priori
information about the presence (absence) and location of unit roots.
In this study, we have investigated causal relations between ISP and other
variables using Toda-Yamamoto (1995) approach. This method is applicable under
different scenarios such as the VAR’s may be stationary, integrated of an arbitrary
order, or cointegrated of an arbitrary order. By relying on this property, we have not
performed an investigation on the existence of unit roots or cointegrating relations
among relevant variables. The results of Granger causality test are given in Table
5.1.
Table 5.1
The Results of Pairwise Granger Causality Tests (Toda-Yamamoto Approach)
Null Hypothesis
Lag
Wald
RER does not Granger Cause ISP
8
17.7551
INDTR does not Granger Cause ISP
1
2.9097
FDIGDP does not Granger Cause ISP
1
0.0696
CAGDP does not Granger Cause ISP
4
20.5260
M2IR does not Granger Cause ISP
2
14.4722
TT does not Granger Cause ISP
5
10.9933
INDEU does not Granger Cause ISP
2
0.6849
RCG does not Granger Cause ISP
1
16.0826
CAFDI does not Granger Cause ISP
4
16.9033
TB does not Granger Cause ISP
2
7.3014
Optimal lag length is determined by AIC.
Maximum order of integration in the system is equal to 1
p-value
0.0231
0.0880
0.7919
0.0004
0.0007
0.0515
0.7100
0.0001
0.0020
0.0260
54
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
CAGDP, M2IR, RCG, CAFDI are Granger causes of ISP at %1 level. There is
also Granger causality from RER and TB to ISP at %5 level. Since the hypothesis
of Granger non-causality from INDTR, FDIGDP and INDEU to ISP cannot be
rejected at the conventional level of significant, we conclude that these variables
can be excluded from VAR model for forecasting of ISP.
6. Forecasting of ISP from VAR Model
A crucial aspect of empirical research based on the vector autoregressive (VAR)
model is the choice of the lag order, since all inference in the VAR model is based
on the chosen lag order. Hafer and Sheehan (1989) find that the accuracy of
forecasts from VAR models varies substantially for alternative lag lengths.
Lutkepohl (1993) indicate that selecting a higher order lag length may lead to a
higher mean square forecast errors and choosing a lower lag order may result with
the autocorrelated errors. To overcome this issue, in a recent paper of Granger and
Jeon (2004), they consider the model selection procedures and compare their
forecasting performance by employing a set of monthly US macro series in the level
and first difference forms. They suggest the equally weighted combination of the
forecasts obtained through AR(4) and models specified via Schwarz Criterion (SC)
and Akaike Information Criterion (AIC).
In this study, we consider the forecasting performances of level-VAR models
whose lags are chosen by two information criteria (AIC, SC) and a sequential
testing procedure (M-Wald). Since SC never choose a longer lag length than AIC
for any reasonable and two model selection procedures are used as the most popular
criteria for estimating order of VAR model, we compare ISP forecasting
performance of level-VAR models whose lags are chosen by these criteria.
In M-Wald procedure, optimal lag-length is acquired by testing down from a
maximum 5-lag system until any one of the null hypothesis is rejected at the 5
percent level. After this sequential testing procedure being applied, the appropriate
lag is chosen as 2 in level-VAR model. On the other hand, lag length are
determined as 1 and 5 by SC and AIC respectively.
Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Theil
Inequality Coefficient (TIC) are used to compare the VAR(1), VAR(2) and VAR(5)
models in terms of their ISP forecast performances. These criteria are calculated for
the in-sample and shown in Table 6.1.
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
Table 6.1
Forecast Error Statistics of Competing Models
Models
Root Mean Square Error
Mean Absolute Error
55
Theil Inequality Coefficient
VAR(1)
1.7508
1.2846
0.4732
VAR(2)
1.5774
1.1862
0.3979
VAR(5)
1.4029
1.1322
0.3374
By relying on forecast error statistics, it can be said that the VAR(5) model in
level produces the most accurate forecast for ISP. We can improve our forecasts by
combining the VAR models which have difference lag. We consider only linear
combination of forecasts here. We consider the combination of forecasts using the
following regression allowing assumption of unbiased forecasts with uncorrelated
forecast error:
ISPt = w 0 + w 1f1,t + w 2 f 2,t + w 3 f 3,t + ε t
where f1t, f2t, f3t are forecasts generated by VAR(1), VAR(2), VAR(3). Combined
forecasts are computed from the approaches mentioned above. Forecast error
statistics are found for the in-sample period and summarized in Table 6.2.
Table 6.2
Combined Forecasts
Combined Models
Root Mean Square Error Mean Absolute Error Theil Inequality Coefficient
VAR(1)+VAR(2)
1.5718
1.1826
0.4030
VAR(1)+VAR(5)
1.3977
1.1078
0.3450
VAR(2)+VAR(5)
1.3978
1.1084
0.3451
VAR(1)+VAR(2)+VAR(5) 1.3976
1.1079
0.3450
The results obtained by combining the VAR(1), VAR(2) and VAR(5) models
show that the use of the combination of these models generate relatively better
forecast values than all the other single models. Combination of these models
produces almost 0.38% reduction in RMSE relative to the best single model (i.e.,
VAR(5) model) forecasts. Actual values (ISP) and forecasts (ISPF) obtained from
combined VAR models are graphed in Figure 6.1
Fig. 6.1. Actual and Forecast Values of ISP
12
8
4
0
-4
-8
1988
1990
1992
1994
ISP
1996
1998
ISPF
2000
2002
56
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
7. Predicting Currency Crises Probabilities: Dynamic Logit and Probit Models
We based our definition of crises on %5 threshold point whose value is 4.04. ISP
is expressed as a binary variable and named ISP* it is determined by the following
threshold values:
ISP
*
1
=
0
ISP
ISP
> 4 . 04
≤ 4 . 04
The observations for 1991.Q1, 1994.Q1, 1994.Q2, 2000.Q4, 2001.Q1 and
2001.Q2 are larger than threshold value. Thus, dependent variable ISP* is equal to 1
for 6 observations, 0 otherwise.
To forecast currency crises probabilities, we estimated logit and probit models
using the lagged independent variables, which are RER, CAGDP, TB, M2IR, RCR,
CAFDI. Since the lagged explanatory variables RCR and CAFDI are not
statistically significant at 10% level for all lags (when the maximum lag length is
selected as 5), they are eliminated in our models. While RER and M2IR are
statistically significant for only lag one, CAGDP and TB are obtained statistically
significant for only lag three at %10 level. Estimation results of logit and probit
models are given in Table 7.1.
Table 7.1
Estimation Results of Logit and Probit Models
Dependent Variable: ISP*
Sample (adjusted): 1985Q4 2003Q4
Logit Model
Variable
Coefficient
z-Stat.
RERt-1
-0.000030
-2.466175
CAGDPt-3
-1.335922
-1.847269
TBt-3
-0.001964
-2.285944
M2IRt-1
0.813738
1.826321
Probit Model
Variable
Coefficient
z-Statistic
RERt-1
-0.000016
-2.681253
CAGDPt-3
-0.725518
-1.821190
TBt-3
-0.001041
-2.430522
M2IRt-1
0.454529
1.894993
Table 7.2 shows the observed and predicted values from the logit and probit
models when the success cutoff point is equal to 50%. The logit model is more
effective at predicting 0’s than the probit model. Nevertheless, two models have
57
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
same percent accuracy at predicting 1’s. The adjusted count R2 is equal to 0.80 for
the logit model whereas 0.67 for the probit model.
Table 7.2
Goodness of Fit Statistics
The Results of Logit Model
The Results of Probit Model
Estimated Equation
*
ISP =0
*
P(ISP =1)<=0.5
*
67
*
ISP =1
Estimated Equation
*
ISP =0
ISP*=1
Total
*
66
1
67
*
Total
1
68
P(ISP =1)<=0.5
P(ISP =1)>0.5
0
5
5
P(ISP =1)>0.5
1
5
6
Total
67
6
73
Total
67
6
73
Correct
67
5
72
Correct
66
5
71
% Correct
100
83.33
98.63
% Correct
98.51
83.33
97.26
Adj. Count R2=0.80
Adj. Count R2=0.67
QSP=0.04092
QSP=0.04329
We also calculate a summary measure of goodness of fit for currency crises
based on a suggestion by Brier (1950). Brier’s quadratic probability score is
calculated as
QSP
*
=
2
T
∑
T
t =1
( IS P̂
*
− ISP
*
)2
where ISP̂t stands for estimated probability of currency crises at time t and T is
sample size. Note that QSP is scaled so that it lies between 0 and 2. QSP acts as a
rough analogue of RMSE, with value of QSP close to 0 indicating greater accuracy
of forecast (Kumar et al., 2003). The QSP values for logit and probit model in %5
crises definition are calculated as 0.04092 and 0.04329 respectively. Comparing
the goodness-of-fit of logit and probit models in sample, it is evident that the former
is better in all cases. Therefore, logit model is preferable for the prediction of
currency crises in terms of probability. The probabilities obtained by the probit
model are given in Table7.3. In the logit model out of 68 no crisis period 67 and out
of 5 crisis period 5 were predicted successfully.
58
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
Table 7.3
The Probabilities of Currency Crises Obtained from Logit Model
1993Q3
Probabilities of
crises
0.003936
1994Q4
Probabilities of
crises
0.000012
1986Q4
0.002470
2003Q1
0.000010
0.919430
1993Q2
0.001744
1995Q4
0.000006
2000Q4
0.748343
1987Q1
0.001665
1997Q2
0.000003
2001Q2
0.674739
1992Q1
0.001643
1999Q3
0.000003
1991Q2
0.475442
1997Q4
0.001391
1988Q4
0.000002
2001Q3
0.299383
2003Q3
0.001154
1989Q1
0.000000
1996Q3
0.283900
1996Q1
0.000959
2003Q2
0.000000
1991Q3
0.144769
1992Q4
0.000783
1995Q3
0.000000
1991Q1
0.054221
1996Q2
0.000680
2002Q4
0.000000
2003Q4
0.043525
1991Q4
0.000426
1989Q2
0.000000
1992Q2
0.041749
1987Q3
0.000413
2001Q4
0.000000
1993Q4
0.023316
1987Q2
0.000358
1989Q4
0.000000
2000Q1
0.020545
1994Q3
0.000311
1999Q4
0.000000
1992Q3
0.019561
2000Q2
0.000270
2002Q3
0.000000
1993Q1
0.018197
1998Q2
0.000232
1989Q3
0.000000
1999Q1
0.017031
1997Q3
0.000169
2002Q2
0.000000
1997Q1
0.012753
1996Q4
0.000163
1995Q1
0.000000
1988Q3
0.008771
1999Q2
0.000139
1995Q2
0.000000
1998Q1
0.008129
1990Q2
0.000083
2002Q1
0.000000
1990Q4
0.007915
1988Q1
0.000075
1985Q4
0.000000
1987Q4
0.007488
1990Q3
0.000040
1986Q1
0.000000
1998Q3
0.007241
1988Q2
0.000031
1986Q2
0.000000
1998Q4
0.006564
1990Q1
0.000013
1986Q3
0.000000
2000Q3
0.006537
2001Q1
Probabilities of
crises
0.998641
1994Q1
0.985230
1994Q2
Periods
Periods
Periods
8. Conclusion
Currency crises are formidably expensive; even more so is a history of recurrent
crises. The costs arrive in three ways: a substantial increase in public debt
associated with the crises, a loss of output and distributions, and the possibility of
socially controversial redistribution of income and wealth.
In currency crises, because the government will bail out banks and often even
companies, public debt increases substantially and with those future tax liabilities.
The deterioration in public finance also arises from a period of high interest rates in
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
59
the run up to the crises and in the stabilisation case. It will also arise from the fall in
output and hence tax revenues in the crises period. Moreover, the increases in debt
may itself bear the seeds of future crises if it occurs in a situation where the
government does not have the ability to meet higher debt service burden by taxation
or reduction in spending. There is always a large loss of reserves, which are
sacrificed during the defence part of the crisis. A crisis deteriorates a country’s
credit rating as well (Dornbusch, 2001).
Turkey is facing with currency crises more frequently especially after
liberalizing its financial sector in the post 1980 period. From this perspective it is
very important to analyze whether Index of Speculative Pressure and early warning
indicators are informative or not for Turkey.
In this study first we find the main determinants of the ISP index by using
Granger Causality Test and then we tried to forecast the ISP index by using VAR
model and then instead of levels by using threshold values we attached 1 and 0
values to index numbers and obtained the probabilities of having crises and finally
we compared the performance of dynamic logit and probit models for the prediction
of the probability of a crisis.
This study shows that current account/GDP ratio, M2/international reserves ratio,
real credit growth and current account/foreign direct investment ratio have greater
impact on the ISP index. Also it shows that the combination of
VAR(1)+VAR(2)+VAR(5) models give us better forecast values then other single
models (for the prediction of level values).
After predicting the probabilities of crises (by using probit and logit models) in
the final step we compared the goodness of fit values obtained from both dynamic
logit and probit models. This comparison showed that logit model is generating
better performance than the probit in forecasting currency crises in Turkey. The
probabilities of currency crises obtained from logit model are given in Table.7.3.
The most important contribution of the study is to show that the logit model has
a very high performance in the prediction of Turkish currency crises. It can be used
to foresee forthcoming currency crises.
Also the forecast of the ISP (as a level) is giving very successful results. Figure
6.1 shows that the ISP and forecasted ISP are almost moving together or very close
to each other.
60
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
Foreseeing currency crises is of vital importance especially for countries that are
facing with such crises more frequently. It seems “how crises can be prevented is
more important than “how to manage a crisis once it occurs”.
But it seems near-impossible to create models that neither miss too many crises
that have occurred nor predict too many that never happen. This makes some
economists highly sceptical of all early-warning indicators. Richard Portes, an
economist at the London Business School, calls them “one of the most egregious
examples of data-mining in all of empirical economics”.
That may be too harsh. If nothing else, the models help policymakers to keep an
eye on indicators that have proved prescient in the past and give traders yet another
number to track. But, as the University of Maryland’s Ms. Reinhart admits: “It is
naive to think that these things can help predict the exact timing of a crisis.” For
investors and traders such models should, at most, be an extra tool, not a substitute
for country analysis and market judgment. As another economist involved admits:
“They are fancy tools, but I wouldn’t trade on them.” (The Perils of Prediction,
1998).
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
61
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S. Edwards and A. Frankel, National Bureau of Economic Research, The University of Chicago
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Çepni, E. 2003. The Economy of Turkey in Retrospect, Beta Basım Yayım A.Ş.
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Markets, ed S. Edwards and J. A. Frankel, National Bureau of Economic Research, The University of
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62
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
Appendix
Table 1
The Weighted Index of Speculative Pressure
ISP
ISP
Period
(Weighted) Period (Weighted)
1985.Q2
0.0225
1985.Q3
1985.Q4
Period
ISP
(Weighted)
Period
ISP
(Weighted)
1995Q2
-1.4633
2000Q2
0.5917
1990Q2
-0.1800
-0.5463
1990Q3
-0.2300
1995Q3
-0.8274
2000Q3
0.5810
0.8504
1990Q4
0.9254
1995Q4
3.2880
2000Q4
4.9133
1986Q1
-0.1264
1991Q1
4.0800
1996Q1
1.6002
2001Q1
4.1100
1986Q2
-0.4518
1991Q2
0.7714
1996Q2
0.4321
2001Q2
4.1991
1986Q3
0.0150
1991Q3
1.4492
1996Q3
0.5226
2001Q3
-0.2930
1986Q4
-0.1450
1991Q4
1.1377
1996Q4
1.5947
2001Q4
0.3293
1987Q1
-0.3830
1992Q1
2.6092
1997Q1
1.7022
2002Q1
-1.8903
1987Q2
1.2304
1992Q2
0.6451
1997Q2
1.1961
2002Q2
-1.1166
1987Q3
0.9392
1992Q3
-1.2370
1997Q3
0.3785
2002Q3
0.7788
1987Q4
-1.2037
1992Q4
0.6346
1997Q4
1.8902
2002Q4
-0.9579
1988Q1
2.8989
1993Q1
-0.0513
1998Q1
0.7520
2003Q1
0.1539
1988Q2
1.4786
1993Q2
0.8940
1998Q2
-0.4680
2003Q2
-1.6636
1988Q3
0.2733
1993Q3
1.2042
1998Q3
1.3143
2003Q3
-2.7032
1988Q4
3.6460
1993Q4
1.8441
1998Q4
1.7456
2003Q4
-0.8170
1989Q1
0.8736
1994Q1
6.7920
1999Q1
0.8344
2004Q1
-1.1946
1989Q2
-1.7301
1994Q2
9.0998
1999Q2
1.2734
2004Q2
0.4442
1989Q3
-1.8215
1994Q3
-6.0236
1999Q3
0.1492
1989Q4
-1.3328
1994Q4
0.7213
1999Q4
0.4072
1990Q1
-0.3898
1995Q1
-1.1088
2000Q1
-1.4820
Mean
0.6283
Standard Deviation
2.0742
Threshold Value
4.0403
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
Table 2
Indicators of Currency Crises: Single Country Literature
Study
Indicator(s)
63
Comments
Blanco and Garber (1986)
[Mexico, 1973-1982]
(1) Domestic credit growth
Very significant
Cumby and Van Wijnbergen
(1989) [Argentine, 1978-1981]
(1) Domestic credit growth
Very significant
Goldberg (1994)
[Mexico, 1980-1986]
(1) Domestic credit growth;
(2) Exchange rate misalignments;
(3) Relative prices;
(4) External credit; (5) Demand for money
Very significant: (1),
(2); Significant: (3),
(4), (5)
Pazarbaşıoğlu and Ötker
(1997) [Mexico, 1982-1994]
(1) Domestic credit; (2) Real exchange rate;
(3) Foreign reserves; (4) Real output growth;
(5) Inflation differential; (6) Expansionary
monetary and fiscal policies
Very significant
Ötker and Pazarbaşıoğlu
(1997)
[1992-1993 ERM crisis:
Belgium, Denmark, France,
Ireland, Italy, Spain]
(1) Domestic credit;(2) Budget deficit;
(3) Unemployment rate;
(4) Foreign price level
Significant: (1)-(4)
for all except Denmark
64
Elif Çepni and Nezir Köse / Central Bank Review 1 (2006) 37-64
Table 3
Indicators of Currency Crises: Multi-Country Literature
Study
Indicators
Comments
Frankel and Rose
(1) Debt composition [commercial bank,
(1996) [Over 100 concessional, variable-rate, short-term, FDI,
countries, 1971-1992] Public sector]; (2) External variables
[international reserves to mothly imports,
current account, external debt, real exchange
rate]; (3) Domestic macroeconomic variables
[government budget, domestic credit growth,
real output per capita growth]; (4) Foreign
interest rate; (5) Developed countries growth
rate
Significant: FDI, international
reserves, domestic credit
growth, foreign interest rate;
real exchange rate; Not
significant;
government
budget, current account
Klein and Marion (1) Macroeconomic variables [real exchange
(1997) [17 countries, rate; net foreign assets, multiple exchange
rate]; (2) Structural factors [openness,
1957-1990]
geographical trade concentration]; (3) Political
factors [executive transfers, coups]
Significant: real exchange
rate, openness, geographical
trade concentration, executive
transfer
Esquivel and Larrain 1) Seignorage; (2) Current account balance;
(1998) [30 countries, (3) Terms of trade shock; (4) Real Exchange
1975- 1996]
rate; M2/Reserves; (5) Per capita income
growth; (6) Contagion effects
Significant: seignorage, real
exchange rate, terms of trade
shocks, contagion, current
account balance, international
reserves, income growth
Kaminsky, Lizondo, (1) International reserves;(2) Domestic credit; Very significant:
Reinhart (1998) [20 (3) Domestic inflation; (4) Real exchange rate; Significant: (6)-(10)
countries, 1970-1995] (5) Credit to public sector; (6) Trade balance;
(7) Money growth; (8) Fiscal deficit; (9)
Export performance; (10) Real GDP growth
(1)-(5);