Download Vulnerability Index – Guessing the Probability of a Currency Crisis

Vulnerability Index – Guessing the Probability of a Currency Crisis
Central European Experience
Ján Tóth*
ING Bank, Slovakia
Abstract
The recent years have witnessed a dramatic increase in the participation of foreign investors in both emerging-market
equity and fixed-income investments. While the rewards in investing in emerging markets are great, so are the risks. A
number of currency crises shook investors’ confidence in emerging countries. We ask whether some of the crises could be
anticipated by looking at economic fundamentals. We have constructed a weighted “vulnerability” index to signal just
that – do economic fundamentals justify a higher probability of a currency crisis occurring in a given country? It is
computed as a weighted average of macroeconomic indicators such as the real exchange rate, the export growth rate, the
ratio of M2 to official international reserves, real domestic credit growth, financing gap, etc. The higher the value, the
higher the model-implied probability of a currency crisis. However, the index is not a direct measure of probability. The
index explains some of the currency pressures in Central European countries.
Motivation
The recent years have witnessed a dramatic increase in the participation of foreign investors in both emergingmarket equity and fixed-income investments. While the rewards in investing in emerging markets are great, so are
the risks. A number of currency crises shook investors’ confidence in emerging countries. We ask whether some of
the crises could be anticipated by looking at economic fundamentals. We have constructed a weighted
“vulnerability” index to signal just that – do economic fundamentals justify a higher probability of a currency crisis
occurring in a given country?
Introduction
In the majority of cases, currency crisis in emerging markets are preceded by a number of economic problems that
warn of the need for an eventual corrective adjustment of exchange rates. Crisis-prone currencies typically display a
number of classic symptoms that warn of an impending speculative attack. These symptoms include excessive real
appreciation of the currency, weak domestic economic growth, rising unemployment, an adverse terms-of-trade
shock, a deteriorating current account balance, excessive domestic credit expansion, banking-system difficulties,
unsustainably large government budget deficits, overly expansionary monetary policies, a high ratio of M2 to
reserves, foreign-exchange reserve losses, falling asset prices, etc. [3]. The economics research has not traditionally
focused on the issue of currency collapses and it has been only for the past few years that the researchers began
collecting the evidence. This could be one reason why many currency turmoils were not anticipated by market
players since in most cases the interest-rate differentials failed to widen several months before the crises [3]. The
past experience suggests that there seems to be some threshold point in the minds of market players beyond which
the country starts to be very vulnerable to the currency crisis and the foreign exchange traders suddenly are
concerned with the country’s deteriorating fundamentals.
In fact, the interest rate differentials sometimes moved in the opposite direction as they should have. Let us
assume that interest rate parity holds, i.e. interest rate differential between the two currencies is equal to the expected
exchange rate change. In case of a fixed exchange rate regime, the longer the regime is in place untouched, the
higher the belief it will stay so since investors often extrapolate the favourable history into the future. Interest rates
converge as the capital flows drive the differential closer to zero. In case of bad economic policy 1 , the productivity
differentials might not be so favourable for the emerging country to offset the rising real appreciation of the
currency. In such a scenario, interest rate differential would actually get smaller as the probability of exchange rate
pressures mounts.
With the efforts to seek higher returns, the investments in emerging countries became fashionable in the
1990s. Portfolio managers have often put money into countries that had not been researched deeply enough. As
Calvo (1995) argues the marginal gain from gathering new information declines as diversification opportunities rise.
In other words, diversification breeds ignorance [3]. Then the attack on such a currency supports a sudden effort on
the side of market players to brush on fundaments and foreign exchange contagion could take its toll on other
vulnerable countries.
The numerous research studies have found that the single best predictor of a future strong currency
correction is the real exchange rate [1,3]. Real effective exchange rate is the trade-weighted nominal exchange rate
adjusted for the difference in domestic and foreign inflation. Another useful indicator includes international
reserves, the ratio of money supply to foreign reserves, M2 multiplier, and so on (see Table 1). However, some of
the indicators were more consistent at pin-pointing the crisis as the others. Table 2 ranks the macro indicators by
their noise – e.g., real exchange rate is much less nosier than M2 multiplier. This is an important criterion as well
since the false signals could precipitate an otherwise healthy emerging country into crisis on a self-fulfilling
prophesy. Therefore, currency vulnerability models should, as much as possible, prevent giving false signals to
market participants.
Table 1: LEADING INDICATORS OF CURRENCY CRISES – GOOD SIGNALS
Actual Good Signals/Possible Good Signals
Real Exchange Rate
25
International Reserves
22
M2/Reserves
21
M2 Multiplier
20
Banking Crisis
19
Terms of Trade
19
Exports
17
Stock Prices
17
Output
16
Excess M1 balances
16
Bank Deposits
16
Real Interest Rate
15
Domestic Credit/GDP
14
Lending Rate/Deposit Rate
13
Real Interest-Rate Differential
11
Imports
9
Source: Kaminsky, Lizondon and Reinhard (1998)
The methodology – the Indicators
IMF in its May 1998 World Economic Outlook report constructed a vulnerability index for six Asian and four Latin
American countries using a weighted average of three variables: real exchange rate, the real domestic credit growth,
and M2/Foreign Reserves ratio [1]. The deviations from their three-year mean levels were inputted into the model.
We have tried to extend the pool of variables mainly suggested by Tables 1 and 2. We assigned the weights to the
variables below so as to be proportional to the ratio of good signals statistics in Table 1 to the bad signal one in
Table 2, where available – see Table 3.
Table 2: LEADING INDICATORS OF CURRENCY CRISES – BAD SIGNALS
Actual Bad Signals/Possible Bad Signals
Real Exchange Rate
5
Banking Crisis
6
Exports
7
Stock Prices
8
Output
8
Excess M1 Balances
8
Domestic Credit/GDP
9
M2/Reserves
10
Real Interest Rate
11
Real Interest-Rate Differential
11
Imports
11
International Reserves
12
M2 Multiplier
12
Terms of Trade
15
Bank Deposits
19
Lending Rate/Deposit Rate
22
Source: Kaminsky, Lizondon and Reinhard (1998)
Table 3: MODEL INPUTS WITH THEIR WEIGHTS
Real Exchange Rate
Exports
M2/Official Reserves
Dollarization Ratio*
Monthly Import Cover
Trade Deficit
Domestic Credit
Cost of External Financing*
Financing Gap*
Total
Weight
25.6%
12.4%
10.7%
10.2%
9.4%
8.3%
8.0%
7.7%
7.7%
100.0%
Note: (*) The indicator does not appear in some form in Tables 1&2 and its weight is given arbitrarily.
Real Effective Exchange Rate
The strength of the currency is the single best indicator. In the previous version of our model applied to Slovakia
only (see Toth (1999)), we took real effective exchange rate (REER) adjusted by producer prices using 9 foreign
countries covering 80% of Slovak exports. We arbitrarily picked January 1995 period as base period due to the
external stability. In the current model, we choose to de-trend REER and we assign the
‘overvaluation/undervaluation’ meaning to the difference between REER and the trend. The advantage of the
approach is that a base period does not have to be chosen and we - at least indirectly - account for some real
sustainable appreciation of the emerging currency due to the higher productivity growth2 . Another possibly better
but more data intensive approach would be to compute real exchange rate on the basis of unit-labour costs.
Banking Crisis
The previous version of the model had the ratio of classified ‘bad’ loans to total local currency loans as an indicator
of a probability of a banking crisis. However, we chose to omit the variable as the numbers have not been reliable in
Slovakia (and probably in other Central European countries). Strong political constituencies have had vested
interests in state banks and banking supervision and the data has probably not been immune to these stakes.
Exports
Export growth signals the ability of the economy to repay its foreign liabilities. Due to the earlier data availability
(current account data are usually released with a quarter delay), we take merchandise exports adjusted by producer
prices to get a proxy for real export growth. We take the 3-month moving average.
M2 to Official Reserves Ratio
The indicator suggests what part of the domestic money supply is backed by the foreign currency. Should the
monetary authorities relax monetary policy and the economy would not be able to attract higher inflows of foreign
reserves due to higher growth, the ratio would send a worrying signal.
Dollarisation Ratio
Dollarisation Ratio points to the proportion of foreign currency deposits to total deposits and thus could indirectly
signal the distrust in domestic currency.
Monthly Import Cover
Monthly Import Cover could be a better indicator than imports in case of a flood of foreign direct investments that
boom both technological imports as well as the foreign reserves. We take the ratio of the latest reserves data to the
average of the last three months of import data.
Trade Deficit
We use the 3-month moving average of trade deficit to expected GDP ratio. Should trade deficit/GDP be bigger than
the ability of the country to pay (proxied by the potential GDP growth) for an infinite period, the country would not
be able to repay the debt as the repayments would exceed output at some point in the future. It is however possible
to get a noisy signal from trade deficit since heavy corporate restructuring based on inward foreign direct investment
usually means a booming trade deficit.
Domestic Credit
This indicator could show the overheating of the economy caused by monetary policy. We adjust domestic credit
growth by producer prices to get a proxy for real domestic credit growth.
Cost of External Financing
We take the spreads on benchmark sovereign (if available) eurobonds. Raising money abroad at favourable cost
should decrease the country’s vulnerability in the short-term.
Financing Gap
Basic balance is usually interpreted as that part of the current account deficit that is not covered with foreign direct
investment and therefore, the country has to attract the funds (in the amount of basic balance) to cover the gap in
more volatile portfolio or debt inflows. In addition, the country has to repay (or revolve) the principals of foreign
debt falling due. We therefore define the financing gap as the 3-month moving average of the basic balance and debt
service (required inflows would have negative signs).
We define the vulnerability index as the weighted average of these standardised macroeconomic indicators.
The higher the value, the higher the probability of a currency crisis occuring. The index is however not a direct
probability measures. The level of zero implies a certain ‘equilibrium’ value of macroeconomic indicators chosen
ex-ante. No political variables are present in the model.
Small Sample Issues
We standardised the variables by taking differences from their mean and dividing them by their respective standard
deviations. In trying to assign equilibrium to zero value, we sometimes ignored historical means due to short sample
data issue and imposed more ‘equilibrium’ values. In addition, since some variables could have extreme values at
certain times (eg, the level of reserves was extremely small in Slovakia in 1993 and m2/reserves standardised
statistics showed the value of 30), we adjusted the standardisation using the following rationale. The values around
mean (eg, roughly the first 20% of values nearest mean) should provide little information content on vulnerability
while the extreme value contribution should be limited as well. We confined extreme signal (eg, roughly the last
10% of values) at 2 (in previously mentioned Slovak case, we then used 2 instead of 30) using the following
transformation function (see Fig. 1).
2.5
f(x)
2.0
1.5
1.0
0.5
0.0
-0.5
3
2
1
0
-1
-2
-3
-1.0
-1.5
-2.0
-2.5
Fig. 1: TRANSFORMATION FUNCTION OF STANDARDISED VALUES X
Note: On the horizontal axis, x means the standardised value (i.e., in units of standard deviation).
The first derivative of the transformation function is then presented in Fig. 2.
1.2
f'(x)
1.0
0.8
0.6
0.4
0.2
-4
-4
.6
-1
-1.
6
-2.
2
-2.
8
-3.
4
0.2
-0.
4
0.8
2
1.4
2.6
3.2
-0.2
3.8
5
4.4
0.0
Fig. 2: THE DERIVATIVE OF TRANSFORMATION FUNCTION
Note: The values between (-0.25,0.25)3 in the units of a standard deviation represent the area where the
information content of the value is arbitrarily lowered to suggest that it is ‘normal’ to see fluctuations inside such a
narrow interval around a perceived equilibrium. Outside the interval (-1.64,1.64)4 the information content of the
value is lowered again not to have one indicator imposing too much weight on the overall index (say, an extreme
value is perceivable on one indicator due to one-off events or structural changes). Outside interval (-2.2) the
transformation value is set to equal 2 or –2. An example favouring such a transformation would include the case of a
sharp devaluation in Turkey in 2001. The real exchange rate was so undervalued (and hence it would have sent such
a strong positive signal if transformation function had not been used) that could offset the negative signals of all
other variables with the resulting vulnerability index deep in negative territory suggesting almost no probability of a
continued currency crisis (which would not be a reasonable thing to expect in Turkish case in 2001). Intervals were
chosen arbitrarily.
Empirical Results – Central Europe
Figures 3-6 plot the resulting vulnerability indices for Central European countries along with the monthly variances
of the daily changes of their local currency to EUR (with a one month shift). Neither the weights in Table 3, nor the
transformation function was chosen by regression, hence, the following results could be considered as out-ofsample. Since immediately after the crisis, the exchange rate substantially weakens, the reader should not be
surprised with the fall of the index on the month of the crisis or just after.
Slovak experience in Fig.3 suggests that the index performed well and signalled the first devaluation in
mid-1993, May 1997 crisis, October 1998 floating of koruna, and May 1999 currency turmoil. Slovakia had fixed
exchange rate regime will October 1999 (therefore, the attack on koruna in May 1997 is not revealed by volatility).
80%
0.10%
Probability of currency crisis increases
0.09%
60%
0.08%
40%
0.07%
0.06%
20%
0.05%
0%
93
-20%
-40%
94
95
96
97
98
99
Probability of currency crisis decreases
Exchange Rate Volatility -rhs
-60%
00
01
0.04%
0.03%
0.02%
0.01%
0.00%
Fig. 3: SLOVAK EXPERIENCE
In the Czech example (Fig. 4) the indicator correctly pin-pointed the May 1997. The trend of the index
(rather than the level itself) also located the second currency pressures in 1998.
150%
0.10%
0.09%
Probability of currency crisis increases
100%
0.08%
0.07%
50%
0.06%
0%
0.05%
93
94
95
96
97
98
99
00
01
-50%
0.04%
0.03%
Probability of currency crisis decreases
0.02%
-100%
Exchange Rate
Volatility -rhs
-150%
0.01%
0.00%
Fig. 4: CZECH EXPERIENCE
In Hungarian case, the index performed reasonably satisfyingly though the absolute level of zero does not
indicate ‘equilibrium’ situation.
80%
0.10%
0.09%
60%
Probability of currency crisis increases
40%
0.07%
20%
0.06%
0%
-20%
93
94
95
96
97
98
99
00
01
Probability of currency crisis decreases
-40%
0.05%
0.04%
0.03%
-60%
-80%
0.08%
0.02%
Exchange Rate Volatility -rhs
-100%
0.01%
0.00%
Fig. 5: HUNGARIAN EXPERIENCE
Polish results have been mixed especially in 1997 though the signal about the recent crisis appeared to be
very reliable.
150%
0.10%
Probability of currency crisis increases
0.09%
100%
0.08%
0.07%
50%
0.06%
0%
0.05%
93
94
95
96
97
98
99
-50%
Probability of currency crisis decreases
-100%
00
01
0.04%
0.03%
0.02%
Exchange Rate Volatility -rhs
-150%
0.01%
0.00%
Fig. 6: POLISH EXPERIENCE
Conclusion
The vulnerability index has been successful at signalling some of the Central European currency distresses,
especially since the nature of the experiment was out-of-sample. This means that similar phenomena have been at
work in Central Europe than in other emerging countries. Due to the problems estimating ‘equilibrium’ values, both
the trend and the absolute value of the index have explanatory power.
Further Research
It might be informative to let the regression choose the weights and see the implied in-sample suggested weights.
Transformation function could be adjusted as well according to the data. REER as the most important variable with
almost 26% weight could be computed using the unit-labour costs. In addition, one could think about bringing in a
new political variable signalling the probability of a government collapse or plainly the time before the elections. In
addition, statistical analysis could be used to transform the index to a probability measure and thus allowing crosscountry comparisons.
Notes
* I would like to thank Alexander Chudik at ING Bank for tremendous help with the data.
1
Such as mismanagement of investments with the state’s influence on banks, the lack of good corporate
governance, or elections-induced fiscal extravaganzas.
2
Balassa-Samuelson argument suggests that assuming convergence, the converging country is bound to
have higher inflation as well as higher productivity growth. We assume the trend represents a linear proxy of the
long-run sustainable real appreciation of a currency due to the higher productivity differential.
3
Corresponding to 20% density under normal distribution.
4
Corresponding to 90% density under normal distribution.
References
[1]
[2]
[3]
[4]
IMF, “World Economic Outlook”, May 1998
Kaminsky, Lizondo, Reinhart, “Leading Indicators of Currency Crises”, IMF Working Paper, July 1997
Rosenberg, “Currency Crises in Emerging Markets”, Merrill Lynch, July 1998
Toth, “Vulnerability Index – Probability of a future monetary crisis”, BIATEC, Slovak National Bank, 1999, No.
10
[5] Calvo, “Varieties of Capital – Market Crises”, Inter-American Development Bank Working Paper Series, No.
306