Models of Economic and Financial

Crises*

ROBERTO S. MARIANO

BULENT N. GULTEKIN

SULEYMAN OZMUCUR

TAYYEB SHABBIR

R.S. Mariano is Professor of Economics and Statistics at the Department of Economics at the University of Pennsylvania; B.N. Gultekin is Professor of Finance at the Wharton School, University of Pennsylvania; S Ozmucur is Professor of Economics and Econometrics at the Department of Economics at Bogazici University, also Department of Economics at the University of Pennsylvania; T. Shabbir is Chief of Research at the Pakistan Institute of Development Economics and Visiting Professor at the Department of Economics, University of Pennsylvania.

* Paper to be presented at the Middle East Economic Association in the ASSA Meetings, January 7—9, 2000, Boston, MA. The authors want to thank Sean Campbell for writing the FORTRAN algorithm, Professor Emre Alper of Bogazici University and Boragan Aruoba of University of Pennsylvania for data collection. Financial support provided by the Economic Research Forum for the Arab Countries, Iran and Turkey (ERF Research Project No: ERF99-US-4004) is also gratefully acknowledge.

1.0 Introduction

The decade of the 1990s was certainly marked by a rather unusual number of financial and economic crises. Amongst the prominent such crises were the near breakdown of the Exchange Rate Mechanism of the European Union in 1992-93, the Mexican (“Tequila”) Crisis in 1994-95 and, finally, the near catastrophic South East Asian Crisis of 1997 with its contagion influence spreading to Russia and South America in 1998. It was but natural that these crises should give rise to a number of important questions. A partial list of such queries might include questions regarding the causes of such financial and economic crises, an historical comparison of the causes as well as the impact of such crises, the contagion effects of 1the 1997 Asian Crisis, the ‘optimal’ level, speed and sequencing of financial liberalization especially that of global capital flows and, finally, the appropriate role of macroeconomic management. Also, in the wake of these recent episodes, the very important question of the need and the feasibility of predicting of such crises has been brought to the forefront.

The objective of the present paper is to focus primarily on the last issue enumerated above, namely, the modeling of an appropriate early warning system for the various financial and economic crises. Of course, the major impetus for this analysis has been provided by the near cataclysmic and rather unexpected financial and economic crisis that exploded in the summer of 1997 in the hitherto model economies of the South East Asia[1]. Following this crisis, several studies have explicitly dealt with the question of devising a viable “Early Warning System”. Obviously, such a system would have a great appeal for policymakers who may want to be ready with a hopefully effective set of preemptive measures for possible future use.

In terms of the structure of the remaining paper, following a brief discussion of the kinds of crises of interest, an overview of the relevant methodologies employed by the existing studies of the Early Warning Systems, this paper argues for Markov Switching modeling as a new methodological approach to the issue of predicting financial and economic crises. A prototype Markov Switching Model is then applied to the case of Turkey and its empirical results are then discussed. The paper concludes with some thoughts on the policy implications in the light of our model’s results.

2.0 Types of Crises

The different types of economic and financial crises that may be of interest range from the “garden variety” currency crises to banking crises, international debt crises and asset prices collapse such as in the stock or the real estate market. In fact, IMF’s World Economic Outlook (1998) offers a succinct and useful characterization of the relatively more important types of crises. It essentially identifies three kinds of crises: currency crises, banking crises (which may lead to systemic financial crisis with spillovers to real sector) and foreign or external debt crises. A brief description of each follows:

According to the IMF’s definition, a currency crisis not only constitutes an actual, observed “severe” devaluation, but also includes cases where authorities are apparently able to fend off actual devaluation but only after substantial increase in domestic interest rate and/or expending international reserves.

The banking crises are perhaps the hardest to identify but are proxied by a large increase in the ratio of non-performing loans, illiquidity and impending or actual insolvency of banking and credit institutions. These banking crises on occasion may degenerate into the “systemic financial crises” which are “potentially severe disruptions of financial markets that, by impairing markets’ ability to function effectively, can have large adverse effects on the real economy”.

Finally, the foreign debt crisis, somewhat of a relic of the 1980s, is characterized by “excessive” sovereign debt burden as measured in terms of ‘national ability to pay’ as indicated by foreign debt to GDP or foreign debt service to export earnings of a country.

The various types of crises described above not only differ in terms of their characterization but also other relevant aspects. For instance, the banking crises tend to precede currency crises, but they could occur simultaneously with systemic financial crises as in the case of the Asian Crisis of 1997-98. The currency, banking and financial crises tend to be ranked in that order in terms of the severity as measured by lost output and time for recovery.[2] Again, there may be important social costs besides the traditional financial costs of such crises (Shabbir (1999)).

While recognizing the important differences that characterize these various kinds of crises, it may be noted that studies of such crises exhibit methodological commonalties, which might as well be highlighted here.

3.0 Review of The Relevant Literature

Below we discuss in turn the relevant empirical and theoretical literature regarding the various kinds of financial crises.

3.1 Review of Empirical Literature

There are essentially two alternative methodologies that have been employed in the empirical studies of the early warning systems for different kinds of crises.

(a) The relatively more popular approach is to use probit or logit models. (As illustrated by Eichengreen and Rose (1998) for currency crisis, Demirguc-Kunt and Detragrache (1998) for prediction of banking crises and Edwards (1984) for an analysis of the determinants international debt reschedulings.)

(b) Alternatively, the methodology adopted by Kaminsky and Reinhart (1996), and Kaminsky, Lizondo and Reinhart (1998) is known as the “signals” approach which essentially optimizes the signal to noise ratio for the various potential indicators of crisis.

Here we will briefly discuss only two representative studies – the first one dealing with currency crises which employs the ‘signals approach’ and the second one with banking crises that uses a logit/probit framework. [3] (Both these approaches are discussed in some detail later in this paper on the section on ‘Methodology’).

The representative study in the ‘signals’ approach genre is by Kaminsky et al. (1998) which presents a review of literature, discusses methodological issues and, in particular, reports on and extends Kaminsky and Reinhart (1996), who examine 76 currency crises from a sample of fifteen developing and five industrial countries during 1970-1995.

The so-called ‘signals’ model essentially involves monitoring the evolution of several key economic and financial variables or indicators that tend to exhibit an unusual behavior in the periods just preceding a crisis. More specifically, Kaminsky et al. (1998) examine over a dozen candidate indicators such as the real exchange rate, stock prices, natural output and bank deposits as the potential precursors of currency crises. They analyze these indicators using the criteria to minimize the ratio of noise (“bad signals as a percentage of potential bad ones”) to signal (“good signals as a percentage of potential good ones”). They also judge these individual indicators in terms of their predicted lead time and persistence of their signals prior to the onset of the crisis. More importantly, Kaminsky et al. (1998) chose the “best” two indicators in order to construct their “Leading Index of Currency Crises” by using a weighted average of monthly (year on year) percentage changes in the real exchange rate and the (negative of) monthly (year on year) percentage changes in gross international reserves. The weights are chosen so as to equalize the conditional variance of these components. This composite index is dubbed as the index of “exchange market pressure”.[4] The periods in which this index exceeds its mean by more than three standard deviations are defined as crises periods.

3.1.1 Determinants of Currency Crises – Empirical regularities

The findings of the empirical regularities as culled from the twenty five studies that are reviewed by Kamisky et al. (1998) are reported in the following Table 1 where the various potential indicators or determinants of the currency crises have been grouped into broader classes such as those representing Capital Account or Financial Liberalization.

Table 1 shows the number of studies in which the particular indicator was found to be significant in at least one of the tests conducted. An assessment of the overall results as summarized in the above table does not provide “a clear-cut answer concerning the usefulness of each of the potential indicators of currency crisis”. This is in large part due to the disparate nature of the studies in terms of their relevant factors considered in the specification for the different studies, procedure to measure those variables and periodicity of the data. Also, at times, even when some variables were significant in the univariate tests they fail to be significant in the multivariate tests. However, on the positive side, Kamisky et. al (1998) favor drawing the following tentative conclusions from these group of studies:

1. Given the fact, that the currency crises may be preceded by multiple economic and even political problems, the modeling of currency crisis prediction should involve a relatively broad range of indicators.

2. The variables that receive ‘ample’ support as useful predictors of currency crises include: international reserves, the real exchange rate, credit growth, credit to the public sector and domestic inflation. The results also lend support for including the trade balance, export performance, money growth rate, M2/International reserves, real GDP growth and the fiscal deficit as potential early warning indicators. On the other hand, the variables associated with the external debt profile or the current account balance did not fare well.

As mentioned earlier, besides presenting a relatively comprehensive review of the some of the earlier work related to prediction of currency crisis, Kamisky et al (1998) also presents an extension of previous work which employs the ‘signals’ approach to identifying and predicting currency crises. Based on empirical results for a sample of fifteen developing countries and five industrial ones during 1970-95, the authors report that the variables with the best track record in anticipating crises include output, exports,

Table 1. Performance of Indicators

Number of Studies Statistically Significant

Sector Variables Considered Results

Capital account international reserves 11 10

short-term capital flows 2 1

foreign direct investment 1 1

capital account balance 1 --

domestic-foreign interest

differential 2 1

Debt-profile foreign aid 1 --

external debt 1 --

public debt 1 1

share of commercial bank loans 1 --

share of concessional loans 1 1

share of variable-rate debt 1 --

share of short-term debt 2 --

share of multilateral develop-

ment bank debt 1 --

Current account real exchange rate 12 10

current account balance 6 2

trade balance 3 2

exports 3 2

imports 1/ 2 1

terms of trade 2 1

export prices 1 --

savings 1 --

investment 1 --

International foreign real GDP growth 1 --

foreign interest rates 3 1

foreign price level 2 1

Financial liberal-

ization real interest rates 1 1

credit growth 7 5

lending-deficit interest

spread 1 --

money multiplier 1 1

Other financial parallel market premium 1 1

central parity 1 1

position within the band 1 1

money demand-supply gap 1 1

change in bank deposits 1 --

central bank credit to banks 1 1

money 3 2

M2/international reserves 2 2

Table 1. Performance of Indicators (concluded)

Number of Studies Statistically Significant

Sector Variables Considered Results

Real sector inflation 2/ 5 5

real GDP growth or level 8 5

output gap 1 1

employment/unemploy-

ment 3/ 3 2

change in stock prices 1 1

Fiscal fiscal deficit 5 3

government consumption 1 1

credit to public sector 3 3

Institutional/ multiple exchange rates 1 --

Structural exchange/capital controls 4/ 2 1

openness 1 1

trade concentration 1 --

banking crisis 1 1

financial liberalization 2 1

months spent on peg 1 1

past foreign exchange

market crisis 5/ 1 1

past foreign exchange

market event 6/ 1 --

Political government victory 1 --

government loss 1 1

legal executive transfer 1 1

illegal executive transfer 1 1

1/ In the statistically significant results, the rate of growth of imports declines prior to a devaluation.

2/ In one of the statistically significant results, an increase in inflation reduces the probability of an attack.

3/ In one of the statistically significant results, an increase in employment increases the probability of an unsuccessful attack.

4/ In the statistical significant result, the presence of capital controls increases the probability of an unsuccessful attack and reduces the probability of a successful attack.

5/ A past foreign exchange market crisis reduces significantly the possibility of an unsuccessful attack, and increases marginally the possibility of a successful one.

6/ Events include significant changes in exchange arrangements (such as devaluations, revaluations, decisions to float, and widening of exchange rate banks); crises overlap with events but include unsuccessful speculative attacks and excludes changes in exchange arrangements not associated with market pressure.

real exchange rate deviations, equity prices and the ratio of broad money to gross international reserves.

Incidentally, Sami (1999) uses a variant of the Kaminsky et al signaling approach in order to analyze the Egyptian case. Based on monthly data from 1961:01 to 1999:03, Sami calculates the conditional probabilities for each value of a composite weighted index whose components are percentage change in real exchange rate and percentage charge in international reserves.[5] A crisis is characterized by a situation where the value of the above index exceeds its mean by two standard deviations.[6]

Further, in a similar vein as in Kaminsky et al, the IMF considers certain specific indicators of macroeconomic and financial vulnerabilities with attendant heightened probability of crisis occurrence. In the context of the onset of the Asian Crisis of 1997-98, the IMF (ex-post) identifies a set of indicators (Table 2) which may have been present in the crisis countries in Southeast Asia (Indonesia, Korea, Malaysia, Philippines and Thailand). It may be noted that while there is some overlap in terms of these ‘indicators of vulnerability’ and the ‘crises predictors’ identified by Kaminsky, et al. (1998), the IMF set is much broader in scope and more cautious regarding our ability to pinpoint a crisis as against merely being vulnerable to one.

3.1.2 Determinants of Banking Crises – Empirical Regularities

On the other hand, the representative study that uses the logit/probit framework is by Demirguc-Kunt and Detragiache (1996) and it deals with predicting banking crises. Based on observations for 1980-94 for a large sample of developed and developing countries, it reported that banking crises tend to occur when the macroeconomic environment is weak especially when growth rate of GDP is low and inflation is high. Also, high real interest rates, balance of payment deficits and presence of deposit insurance scheme were found to be significant precursors of banking crises.

It may be noted that both in the case of the studies of the currency as well as the banking crises, it may be noted that differences in methodologies, time periods covered and selection of countries, as well as disparate definitions of exchange market pressure, pose special challenges for arriving at generally applicable conclusions as to what set of leading indicators of currency and banking crises are likely to prove the most useful? However, it is still possible to arrive at some tentative conclusions about the generally useful indicators of vulnerability. Thus, currency crises tend to be preceded by an

Table 2. Selected Indicators of Vulnerability

(end-1996)

________________________________________________________________________

Indicator* Indonesia Korea Malaysia Philippines Thailand

Macro Indicators

Inflation >5% Yes No No Yes Yes

Fiscal deficit >2% of GDP No No No No No

Public debt >50% of GDP No No No Yes No

Current account deficit >5% of GDP No No No No Yes

Short-term flows >50% current

account deficit¹ Yes Yes Yes Yes Yes

Capital inflows >5% of GDP No Yes Yes Yes Yes

Ratio of short-term debt to international

reserves >1 ² Yes Yes No No Yes

Financial Sector Indicators

Recent financial sector liberalization Yes Yes No Yes Yes

Recent capital account liberalization No Yes No No No

Credit to the private sector >100% of GDP No Yes Yes No Yes

Credit to the private sector,

real growth >20% No No Yes Yes No

Emphasis on collateral when making

loans Yes Yes Yes Yes Yes

Estimated share of bank lending to the

real estate sector >20% 3/ Yes Yes Yes Yes Yes

Stock of nonperforming loans >10% of

total loans No No No No No

Stock market capitalization (as percent

of GDP) 40 30 310 98 56

Source: IMF, International Financial Statistics; World Economic Outlook database; World Bank, IFC Emerging Market database.

*The cut-off points are based on the relevant literature that attempts to predict currency and banking crises (Kaminsky, Lizondo, and Reinhart (1997) for currency crisis, and Hardy and Pazarbasioglu (1998) for banking crisis).

¹ Defined as the sum of net portfolio and other investments in the financial accounts.

² As of June 1997.

³ At end-1997, includes indirect exposure through collateral.

overvaluation of the real exchange rate, rapid domestic credit growth, expansion of credit to the public sector, a rise in the ratio of broad money to foreign exchange reserves, an increase in the domestic inflation rate, a decline of FDI flows, and an increase in industrial country interest rates. Less important factors in this regard are a widening of the trade deficit, and increase in the fiscal deficit, deterioration in export performance, as well as a slowdown in real GDP growth. It may be noted that current account and fiscal deficits do not seem to emerge as important indicators.[7] On the other hand, regarding the banking crises, these are often preceded by large inflows of short-term capital (‘hot money’), rapid expansion of domestic credit (which may result from inadequately sequenced and/or supervised) financial liberalization, recessions, and declines in asset prices such as stocks and real estate. The various case studies suggest that often financial sector liberalization without adequate prior strengthening of the regulatory structure not only sets the stage for a banking crisis but also makes it more difficult to cope with it if one erupts.

3.2 Review of Theoretical Literature

Using the case of the currency crises as an important illustration of the financial crises in general, this section presents a brief overview of the theoretical literature on the causes of currency crises with a special reference to identifying the potential early warning indicators.

The historical development of the theoretical literature can be grouped in three “generations” of models --- each reflecting the distinct mechanism that is espoused as the major cause of such crises. We will discuss these models in turn.

3.2.1 First Generation or ‘Fundamentals’ Models

Epitomized by Krugman(1979), the first generation models tend to focus on the role of economic and financial ‘fundamentals’ such as the unsustainable fiscal policies in the face of the fixed exchange rate as the major cause of an eventual currency crisis. Given a fixed exchange rate regime, the persistent need to finance government budget deficits through monetization would surely lead to a reduction in the international reserves held by the Central Bank. Since such reserves are finite the speculative attack on the currency is the eventual outcome of this scenario.[8]

This rather simple model suggests certain ‘fundamental’ imbalances such as the gradual decline in international reserves, growing budget deficits and domestic credit growth as the potential early warning indicators of speculative attacks. As noted in Abiad(1999), in addition to these indicators, other models in the spirit of the first generation models suggest current account deficit and real exchange rate overvaluations as additional early warning indicators. These reflect the alternative mechanisms that will force the monetary authority to eventually abandon the peg in the face of an expansionary fiscal policy either by leading directly to a worsening of the current account through a rise in the import demand or indirectly through a rise in the relative price of the nontradables (and the subsequent overvaluations of the real exchange rate).8

3.2.2 Second Generation or ‘Self-fulfilling Prophecy’ Models

The development of the so-called Second Generation models of the currency crises were motivated by the EMS currency crisis in 1992-93 where some countries such as the UK and Spain suffered crises despite having adequate international reserves, manageable domestic credit growth and non-monetized fiscal deficits --- characteristics that ran counter to the necessary conditions asserted by the first generation models. Obstfeld (1994) and Krugman (1998) addressed the concerns raised by these counter-examples.

The main innovation of these second generation models lies in identifying the role that the ‘expectations’ of the market agents may play in precipitating currency crises. More specifically, Krugman (1998) notes three elements that characterize the second generation models of such crises. First, there must be reasons such as a preference for price stabilization for which the government would want to defend the peg. Second, at the same time there must be reasons why the government would like to end the peg, say, for the sake of stabilization policy to combat high unemployment. Finally, these models maintained that the relative cost of defending a peg is directly proportional to the people’s expectations that such a peg may be abandoned.[9]

In these second generation models, several aspects of the key role played by the expectations may be noted. First, in these models the government policy both affects expectations and is affected by it ---- this simultaneity gives rise to the possibility of ‘self fulfilling prophecy’ aspects of these models and may generate multiple equilibria. The second interesting aspect of this debate, which has not yet been fully explored in the literature, is concerned with the exact manner in which these expectations are formed or determined. If these expectations are purely psychological based or are formed in the ideal environment of perfect information (regarding the government objective function) and perfect coordination (amongst market participants), one may have difficulty in finding a “tight relationship between fundamentals and crises as sometimes crises may take place without a previous significant change in fundamentals”.[10] However, if, as is likely, market participants have incomplete information about the government’s objective function or its ability to defend the fixed exchange rate and suffer from imperfect coordination (due to important non-linearities in market coordination), fundamental factors such as the current account deficit or credit growth can be the important determinants of the expectations and thus (albeit indirectly) of the likelihood of the occurrence of crises. Thus the second generation models, couched in terms of a government’s objective function and peoples expectations, suggest such early warning indicators of currency crises as the high unemployment, inflation, current account deficit, financial sector vulnerability and domestic debt.[11]

3.2.3 Third Generation or ‘Contagion’ Models

The third generation models are based on the notion of ‘contagion’ where the mere occurrence of a crisis in one country increases the likelihood of a similar crisis elsewhere. As described in Masson (1998), three related scenarios can be identified to represent the paradigm of contagion: ‘monsoonal effects’, ‘spillover effects’ and ‘pure contagion effects’.

The monsoonal effects refer to the case when a common external shock affects all the countries in a region or a group. For instance, the oil price shock (in the 1970s), unexpected increase in the world interest rate (as in the sovereign debt crisis of the 1980s) and more recently, appreciation of the reference currency (U.S Dollar) relative to the other key currencies (Yen, say) which played a big part in the Asian Crisis of 1997. On the other hand, in the case of the ‘spillover effects’ , an unexpected devaluation in a given country may adversely affect the relative international competitiveness of a group of competitors thus initiating a chain reaction in terms of a series of currency devaluations in the affected countries. (For instance, Thai Baht’s devaluation made the exports of the competitors in the region relatively more expensive and raised the speculation of ‘competitive devaluations’ in Malaysia, Indonesia and South Korea). Finally, in the case of the ‘Pure Contagion Effects’, the foreign investors may decide to color all the countries which are perceived to be in a similar situation as the initial victim of a currency crisis with the same (negative) brush—this is tantamount to a ‘herd behavior’. In terms of the potential indicators of early warning suggested by the third, generation models, some of the viable indicators will be variables such as the ‘world interest rate, growth rate of trading partners and measures of relative international competitiveness in the export sector.

4.0 Methodology

As mentioned earlier, the recent efforts at devising an early warning system for an impending financial crisis have taken the form of two related approaches. The first approach estimates a probit or logit model of the occurrence of a crisis with lagged values of early warning indicators as explanatory variables. This approach requires the construction of a crisis dummy variable that serves as the endogenous variable in the probit or logit regression. Classification of each sample time point as being in crisis or not depends on whether or not a specific index of vulnerability exceeds an arbitrarily chosen threshold. For example, for currency crises, the index of vulnerability is sometimes based on a weighted average of percentage changes in nominal exchange rates, gross international reserves and short-term interest rate differentials (e.g. local versus US rates when dealing with crises in the Philippines). Explanatory variables typically would be variables in the real sector of the economy, financial variables, external sector and fiscal variables. This approach has the advantage of providing a framework for statistically measuring the magnitude and significance of the effects of various potential explanatory variables on the onset of a crisis. The estimated model also allows the estimation of the probability of occurrence of a crisis in the future given projected or anticipated values of the explanatory variables. Negative aspects of the approach partly derive from the following:

1. The model does not address the independence of crisis occurrence from period to period – except indirectly through serial correlations that exist in the explanatory variables.

2. Additional serial correlations may even be introduced inadvertently through the explicit manner in which the crisis dummy variable is constructed. For example, the use of exclusion windows (where the crisis variable automatically is set to zero for k periods immediately following a time point rated to be in crisis) establishes perfect correlation between a crisis time point, and the next k periods following it. In general, any serial correlation in the crisis dummy variable which is not taken into account in the probit or logit regression would cause the estimates of the model to be inconsistent.

3. Another source of inconsistency: errors in the construction of the crisis dummy variable leading to misclassification of time points – either a false signal of a crisis or a missed reading of a crisis.

4. The method does not provide a direct measure of the weakness or intensity of the signal of each explanatory variable regarding the onset of a crisis.

The second method uses a signaling approach to get a more direct measure of the importance of each candidate explanatory variable. The approach constructs a similar binary variable from each explanatory variable – thus imputing a one (for crisis) or a zero (no crisis) signal from each explanatory variable at each point in time in the sample. A signal-to-noise is then computed for each explanatory variable over the whole sample period – as a quantitative assessment of the value of the variable as a crisis indicator. This signal-to-noise ratio is defined as the ratio of the success rate of crisis predictions relative to the false alarm rate. More specifically, let n_ij be the sample frequencies (for each explanatory variable) defined as follows:

Actual Prediction	No Crisis	Crisis
No Crisis	n11	n12
Crisis	n21	n22

Then, the signal-to-noise ratio for the indicator variable is

[n₂₂/(n₂₁ + n₂₂)]/[n₂₁/(n₁₁ + n₂₁)].

This approach allows a direct ranking of variables as crisis indicators and provides a quick focus on the source of the crisis (assuming an encompassing set of indicators). But the approach does not take into account strong correlations among indicators, provides no framework for statistical testing or calculation of crisis probabilities in the future, and is still open to misclassification errors that can bias the conclusions of the analysis.

In this paper we propose the Markov Switching Model as the new approach to predicting financial crisis and apply it to the case of Turkey. This methodology avoids the potential misclassification errors in the probit data, addresses the serial correlations inherent in crisis occurrence, allows for measuring and testing significance of indicator variables, delivers forecast probabilities of future crises conditional on projected future values of indicator variables, and short-run forecasts of key macroeconomic variables.

Our proposed approach constructs a quantitative prediction model that consists of two parts:

1. A Markov chain model of the unobservable financial health of a country, say, S_t. We argue that what we observe are indicators of this latent attribute of the country. Initially, we assume two states: normal (S_t=1), and critical (S_t=0). We further assume that this Markov chain is of order 1, with transition probabilities that are time varying through dependence on observable indicator variables. Part of our empirical analysis will deal with identifying the appropriate set of indicator variables, thus identifying early warning indicators of a crisis. Experimentation starts with the indicator variables suggested by earlier studies.

2. A vector autoregressive (VAR) model of key macroeconomic variables—such as GDP or industrial production, inflation, interest rate and exchange rate. This VAR model differs from the usual one in the sense that it includes the unobservable state variable, S_t, as an additional endogenous variable. With the inclusion of S_t, we introduce the notion that the VAR system behaves in a different fashion depending on whether financial conditions are normal (S_t=1) or critical (S_t=0). We reflect this in our model by allowing VAR parameters to change in value over time as financial conditions become normal or critical.

In summary, we are proposing a Markov Switching VAR Model that allows intercepts, lag coefficients and error variances in the VAR model to stochastically switch over time according to the value taken by the Markov chain.

The model is described in further detail below.

Let S_t - Markov chain of order 1 with transition probabilities p_t and q_t. Thus, at any given time t, S_t=0 or S_t=1, and

Pr(S_t=1* S_t-1=1) = p_t, and Pr(S_t=0* S_t-1=0) = q_t

Further, let

Y_t = the vector of endogenous variables that we want to forecast

X_t = the vector of exogenous variables to be used to explain the movements in Y_t,

Z_t = the vector of exogenous variables to be used as indicators of a financial crisis; this may overlap with X_t

Then we also assume that

p_t = F(8NZ_t), q_t = F(*NZ_t), where F(@) is a cumulative distribution function such as for a standard unit normal distribution.

Finally, we complete the model with the specification of the VAR model for Y_t variables:

Y_t = A_1,st Y_t-1 + A_2,st Y_t-2 + … A_r,st Y_t-r + B X_t + g_t,

Note that the lag coefficient matrices A are subscripted by S_t. This says that values are shifting between two sets of possible parameter values: {A_j,0} and {A_j,1} depending on whether the Markov chain is equal to zero or one.

An initial set of explanatory variables (X_t and Z_t) is determined and data are collected for them and for Y_t for a specific country. The Markov Switching Model specified above is then estimated by recursive maximum likelihood methods. Our empirical analysis proceeds from there to assess the statistical significance and relative importance of each explanatory variable as an early warning indicator of a crisis. Diagnostic tests are performed to validate the forecasting ability of the estimated model and to assess its performance in comparison with the earlier approaches.

5.0 Prediction of Financial Vulnerability: The Case of Turkey

5.1 A Brief Overview of Major Economic/Financial Events in the Country

The underlying cause of macroeconomic imbalances in Turkey has been the chronic fiscal deficits[12]. Monetization of the public sector deficits, on the other hand, has been the cause of the inflationary periods. Successive governments ran deficits to implement the economic development and industrialization programs they subscribed. Tax revenues were not sufficient to undertake the ambitious public investment projects while granting generous subsidy schemes to all sectors of the society. Furthermore, domestic savings were not sufficient to sustain the growth and the industrialization objectives. The shortfall was made up with the inflow of foreign capital, and mostly as debt financing. The behavior of the creditors and their willingness to finance the current account deficits, whether through unilateral arrangement, or through private capital flows, was the key to the balance of payment crises in Turkey.

Economic priorities and thus the resulting policies of governments in power during the last four decades merely affected the structure and the composition of public sector deficits. The size of the financial system and the availability of foreign capital would enhance the ability of the treasury to finance the deficit in the short run, but it is not feasible to run deficits indefinitely. Eventually the budget constraints become binding. In some sense, the economic crises in Turkey have been rude reminders of budget constraints that forced involuntary adjustments on the economy by the creditors. The nature of financing of the deficits may affect the path to an economic crisis. The role of economic actors may vary in the process depending on the way the deficits were financed. The underlying economics of the situation, however, remains the same: Unsustainable debt financing of deficits[13].

Three episodes of financial crises over the three decades in Turkey provide us with an invaluable economic lesson: fundamental macro imbalances and financing of these imbalances are the main causes behind the balance of payment crises. Before each episode, the governments tried to finance an economic growth rate that domestic savings cannot sustain. Foreign borrowing made up the difference between the growth rate targets and the sustainable grow rate. When the foreign lenders decided that the country could not service her debt, balance of payment crises erupted. Availability of increased foreign capital when the structural causes of macro imbalances are not removed postponed the timing crises. It gave policy makers in Turkey time to accumulate more debt to continue with their irresponsible fiscal policies. The increased size of the debt in each episode amplified the successive magnitude of the crisis.

The 1980 program was to address the fiscal policy problems of the previous decades in two directions. The new administration wanted to reduce the fiscal deficits by increasing tax revenues while reducing the public spending. They also wanted to cut down the subsidies to the state economic enterprises (SEE) by increasing the prices of their goods and services. Second important experiment was the liberalization of interest rates. Objective was to increase private savings. The ultimate objective of the plan was to reduce the role of the state's involvement in the economy.

There was not a long run improvement on the SEEs efficiency. Direct transfers from the budget to SEEs were reduced immediately after the 1980 program. They were left on their own to take care of their own financing requirements. Privatization of the SEEs was an eventual goal, though it has not yet been realized. While transfers from the budget to SEEs are reduced, financing situation of SEEs did not improved over time. Initial improvement came mostly from price increases by SEEs. Later however, the SEEs resorted to borrowing from domestic and international markets to cover their losses. Government's direct involvement in the economy as measured by the share of public investment did not change significantly from previous decades.

Public sector deficits followed a similar path as in previous decades. Deficits were lower during the initial years of the structural adjustment program. It was mostly due to the introduction of value added taxes in 1985. In 1987, public sector deficits resumed its secular climb until 1993 when PSBR reached an all time high of 12% of GDP[14].

Political cycles are also evident in the deficits. In election years, PSBR figures seem to jump up. It is interesting to note that public sector deficits did not come down after the election of 1991. Demirel came back to power after over decade of political struggle. His political patronage style of running the economy was back with him. Public sector deficits continued to rise. Surge of capital inflows after the elections made the new government more complacent about the not taking a tougher fiscal stand.

Despite the rhetoric of 1980s, the role of the state did not diminish during 1980s measured in terms of public investment in total investment and of central government expenditures in GNP. The change was in the emphasis. Motherland government, as others did in the past, undertook an ambitious program to upgrade the country's infrastructure, especially in the areas of energy, telecommunications, and transportation. Size of infrastructure projects got even larger with the availability of foreign project credits while direct subsidies and various incentive schemes as well as implicit subsidies by implicit condoning of tax evasion supported the private sector. Deficits as before continued but the sources of financing began to change in 1980s.

The difference in financing this decade is the increased availability of foreign capital flows as consequence of financial liberalization programs. Turkey's growth performance since 1980s can be largely attributed to liberal, perhaps somewhat reckless, use of her debt capacity. Both domestic debt and foreign debt continued to rise measured by levels and ratios to the gross domestic product. Unlike Latin American countries during 1980, Turkey was not forced to produce trade surplus to pay off her debts, but allowed to accumulate debt.

Monetary policy during 1980s underwent some changes as well. Financial deregulation was initiated with liberalization of interest rates on bank deposits and eventually complemented with institutional developments.

One of the aims of the 1980 program was to prevent the direct central bank credits to public sector. Central bank credits to SEEs and other public agencies were eventually reduced though they were not entirely eliminated. In order to reduce the short-term financing needs of the Treasury from the Central Bank, government debt instruments were introduced and treasury bills and bonds markets were created. Changes in the Central Bank law empowered it to conduct open market operations as well as setting the foreign exchange rate. The purpose of all these innovations was to provide the Central Bank with additional instruments of monetary policy besides reserve requirements and at the same time to equip the Treasury with additional instruments for debt management to replace the central bank borrowing.

In 1989, the Central Bank and the Treasury announced a protocol to limit the Central Bank of the Treasury financing to 15% of the annual budget appropriations, which was the legal limit. The idea behind the protocol was to force the political authority to limit the monetization of the public sector deficits[15]. Domestic borrowing became an increasingly important source of financing for the Treasury, which was constrained in international debt markets. Under the new policy of appreciating exchange rate, commercial banks found it extremely attractive to finance the Treasury with the short-term foreign debt[16].

In 1989, capital account was fully liberalized. The official explanation for this final step was the final stage of the financial reforms, which began in 1984 with the aim to integrate with international markets. The need to finance of the increasing public sector deficits was also an important motivation to liberalize the capital account. Furthermore, the same following a competitive exchange policy pursued during 1980-88 led to capital loses on foreign debt and deterioration of terms of trade of public sector against the private sector[17].

Under the new regime, as the Central Bank created reserve money against the foreign exchange reserve accumulation, short-term capital inflows became the ultimate financing source of the fiscal deficit with the domestic financial institutions acting as the intermediaries[18]. (See Celasun, Denizer, He (1997))

While relative prices are not fixed by fiat, the behavior of important macro variables became more volatile. As government tried to fix the real exchange rate and the real interest rate to maintain the international competitiveness and to attract foreign capital, with persistent public sector deficits, the result was high and variable inflation, which led to further volatility in real interest rates and real exchange rates.

It is also important to describe the role of the commercial banking sector during this period, that is, after the shift in the exchange rate regime in 1989. The interest rate differentials between the foreign borrowing rates and the government papers created exceedingly tempting arbitrage opportunities for the banks and a number of banks run unhedged foreign exchange positions. The aggregate numbers of the unhedged (or open) for the banking system was $2.9 billion (48% of capital) in 1992 and it went up to $4.6 billion (68% of capital) in 1993. After the financial crisis of 1994, banking sector reduced its unhedged position to $.8 billion (18% of capital). Banks' behavior to close their unhedged position is critical to understand the dynamics of the financial crisis of 1994.

While the Central Bank was empowered with new instrument to conduct the monetary policy, its ability to control the monetary aggregates did not improve significantly. Actually, the Central Bank's ability to control liquidity and monetary aggregates was eroded over time. With financial liberalization, banks were able offer foreign exchange deposits for residents and which resulted in currency substitution. Differential reserve requirements on various bank liabilities not only created additional difficulty to control monetary aggregates; they also rendered most definitions of monetary aggregates meaningless. Inflation rate parallels monetary expansion, in particular the growth rate of currency in circulation[19]. Monetary policy therefore was subservient to the fiscal policy. Primary role of the monetary policy was not to target the price stability, but rather, it was delegated to target the real exchange rate and the real interest rates to prevent the capital flight.

Liberalization of interest rates aimed to establish positive real rates and thus to increase the private savings. Eventually, however, real interest rate policy became captive to the exchange rate policy to prevent capital flights under continuing public sector deficits.

By the end of 1993, we can summarize our observations on the money and banking sector as follow[20].

a. Most definitions of monetary aggregates lost their meaning besides currency in circulation.

b. Seniorage income from currency supply has been significantly eroded. There are two reasons for this. Firstly, Central Bank began providing credit to State Soils Office (TMO) and Agricultural Cooperatives. Secondly, reserve requirements were lowered on most bank liabilities. For some bank liabilities, which are not different from bank deposits, were exempt from reserve requirements[21].

c. The reserve requirements were lowered with the belief that lower reserve requirements would keep the interest rates lower and help the deepening the financial markets[22]. Consequences, however, was loss of control of the Central Bank over monetary aggregates except currency in circulation. By the end of 199, demand for reserve money came to t level of 5 to 7% of national income and to around 45% of total deposits.

d. Foreign exchange deposits reached to a level of 60% of total deposits. Increased level of currency substitution not only weakened the control of the Central Bank on monetary aggregates; it created a parallel monetary expansion mechanism.

It seems clear that fundamentals and the fiscal stance until the 1994 crisis were deteriorating rapidly. The required fiscal discipline to complement the use of exchange rate policy as a nominal anchor was ignored. Further deterioration of the fiscal deficits created an unsustainable policy mix.

The government delayed a stabilization program until the local elections at the end of March. This delay was a serious strategic policy mistake. The delay coupled with sequence of other tactical mistakes triggered the forth-economic crisis of in the Turkish economic history in the first quarter of 1994.

Ozmucur (1991) uses logit and probit models to predict “need for IMF programs” and “bottlenecks” in the economy. Using 1950 to 1991 annual data, it was possible to predict seven out of eight years with “bottlenecks”. Real exchange rate, real interest, external terms of trade, excess demand (money supply growth- real GDP growth), and share of current account balance in GNP, all with one or two lags, are used as explanatory variables in the model[23].

Ucer, VanRijckeghem, Yolalan (1998) examine currency crisis of 1994, and conclude that the crisis was a result of deterioration of economic fundamentals, and not only macroeconomic mismanagement. They use quarterly data and apply the methodology of Kaminsky, Lizondo and Reinhart (1998) and propose some new indicators. In addition to 12 indicators used by KLR, they conclude that ratio of exports to imports, real exchange rate, short-term external debt/GNP, debt maturity, reserves to M2Y plus debt stock, government deficit/GNP, short-term advances to the treasury/GNP.

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[1] Precipitated by de-pegging of the Baht by Thailand on July 2, 1997 which then promptly fell 15% in one day, the wave of currency depreciation quickly spread to other South East Asian countries. By early December, 1997, most of these countries had abandoned any efforts to peg their currencies against the US dollar and by that time their currencies had experienced a depreciation to the tune of 35-40% since the onset of the crisis. Compared to the beginning of the year, the stock prices for these countries suffered setbacks in the range of 20-60% as of December 1997. The rate of growth of real GDP also dropped dramatically for most of these countries.

[2] Again, there may be important differences across emerging and mature economies – based on a sample of 22 industrial and 31 emerging economies, the IMF reports the average recovery time was relatively shorter in emerging economies perhaps due to higher mean and variance in output growth in emerging economies.

[3] For additional related studies see Goldstein (1996), Klein (1998), Lau and Park (1995) and Mishkin (1998).

[4]A similar measure is called “Severity” in Kaminsky and Reinhart (1996), “vulnerability index” in IMF (1998b) which is a weighted average of three variables: deviations of the real exchange rate, the 12-month percent change in real domestic credit, and the ratio of M₂ to foreign reserves. In a similar vein, E. Chengveer, Rose and Wyplosz (1995) include domestic interest rate in addition to the components of the “severity” index of Kaminsky and Reinhart (1996).

[5] The weights of the two components are calculated such that the combined variance effect is standardized as (1) standard deviation of Ex (Ex) + (1/ standard deviation of R) R where Ex and R represent percentage changes in real exchange rate and international reserves respectively.

[6] Unlike Kaminsky et al (1998) where crisis is defined when the weighted index exceeds three standard deviations.

[7] See, Milesi-Ferretti and Razin (forthcoming) for further discussion of current account deficits as predictors of currency crises.

[8] The precise point of such a speculative attack on the currency will correspond to the point where the fixed exchange rate will equal the shadow exchange rate which in this context would be the hypothetical floating rate that may result if the Central Bank were to sell off its remaining international reserves.

[9] As noted in Abiad (1999), this can occur under several alternative scenarios. In the regime of sticky wages, an expected depreciation would be built into the future wage demands resulting in higher unemployment, given the government’s commitment to a fixed exchange rate. An alternative mechanism may result in higher unemployment by increasing the cost of loanable funds as risk adjusted interest rate has to be hiked to defend the peg in the face of the expectations that it will depreciate. This can have a particularly destabilizing influence in an already fragile or vulnerable financial sector as was the case in Thailand and other South East Asian Countries in 1997.

[10] Kaminsky et al. (1997) p. 7

[11] According to Krugman (1998), like the first generation models, the root cause of the second generation models is still the policy inconsistency; however, there is a greater element of government discretion in the latter case where the government may eventually want to rather than have to ( as in the case of the first generation model) abandon the peg.

[12] For a detailed account of economic policies during the last 50 years, see Mariano, Gultekin, Ozmucur, and Shabbir (1999), and Onis & Ozmucur(1989a,1989b)

[13] While the case of Turkey is related to financing of public sector deficits, the East Asian crises demonstrated that unsustainable debt financing by private sector could lend an economy equally susceptible to financial crises.

[14] Spike in 1984 reflects reduction in marginal taxes to offset the bracket creep due to inflation

[15] The Central Bank and the Treasury used every possible public relations means to announce the new policy and the agreement between the two agencies. Civil servants in these agencies lost faith that politicians will ever follow the fiscal discipline required by financial liberalization.

[16] The Central Bank and the government unofficially encouraged banks to pursue this policy.

[17] See Celasun and Arslan (1996) and Celasun, Denizer, and He (1999) on the impact of capital flows on the economic policies in Turkey.

[18] During this period, the Central Bank often acted at times like an unofficial financial advisor for the banking sector. Commercial banks often discuss the terms of their foreign borrowing and seek the Central Bank's unofficial approval in the way described by Dornbush (1996).

[19] Surge of inflation in 1980 is mostly due to deregulation of SEEs prices.

[20] See also celasun (1998), Gultekin (1994) and Ozatay (1996,1998).

[21] Some financial instruments, such as bank liabilities issued against short term credits, were exempt from reserve requirements to deepen the financial markets. Likewise, there are lower or no reserve requirements against borrowings abroad and on deposits of foreigners at Turkish banks. Such differential reserve requirement further encouraged currency substitution.

[22] There is almost a unanimous belief in the banking circles that the lower reserve requirements and tax exemption would lead to lower interest rates for the Treasury and the private sector. It is not clear how valid these arguments for the non-competitive market structure of the Turkish banking sector.

[23] Neftci & Ozmucur (1991a, 1991b) try to find coincident and leading indicators of economic activity based on monthly data for the period 1980 to 1990.. Their coincident indicators are: index of manufacturing production, imports, newly established firms, and railway freight. Real M1 and real M2, real central bank credits to banking sector, the ratio of central bank credits to M2, real value of capital reinvested, total surface area of buildings in new building permits, real value of government’s consolidated budget expenditures, and demand for jobs as reported by Government employment bureaus are eight variables which make up the index of leading indicators. Leading indicators perform well in capturing cycles in economic activity during the 1980-1990 period.