In the existing literature, many definitions of currency
crises have been adopted. Generally, exchange rate, interest rate and
international reserve are the components in these definitions. The authors tend to determine a crisis period
in two ways. The first group bases on the combination of the components. Some
notable examples are the definitions of Eichengreen, Rose and Wypslosz (1996)
and Kaminsky, Lizondo and Reinhart (1998). The advantage of these indexes is
that they consider not only successful speculative attacks (large devaluation
of exchange rates) but also unsuccessful ones (loss of reserves and high
increment of interest rates). This feature comes as a result of a weighted
combination of exchange rates, interest rates and international reserves. The
second group considers the components separately. For instance, the definitions
of Frankel and Rose (1996) and Zhang (2001). The positive feature for applying
this approach is that it fits well in the case when the impacts of three
components cancel out each other. However, it considers only successful attacks
and ignores unsuccessful ones. As a result, it is inconsistent when we aim to
compare these forecasting models with different crisis definitions. Toward a
unified framework for predicting currency crises and following the mainstream
of applying simulation methods to determine the probability of rare events
including the models of Anderson (2007), we make a new approach by using vector
autoregressive model (VAR) for three variables that are exchange rates,
interest rates and international reserves. By doing so, we can apply any
existing definition to obtain a comprehensive crisis forecast given the
forecasted results of the three components.

Most of the conventional models in forecasting currency crises
apply discrete dependent variables coding the tranquil regime as zero and the
crisis regime as unity. The crisis regime is defined at the time when exchange
market pressure index signals a crisis some months (6, 12 or 24 months for
example) prior for the purpose of prediction Bussiere (2006). This approach
shows itself to be illogical because two different periods which are the time
before a crisis and crisis time are tied together and given the same value of
unity. Compounding the problem, the pre-crisis period is chosen arbitrarily
which does not rely on any evidence from the data. In this study, we can avoid
that pitfall by predicting three components in advance from 1-month lead to
6-month lead.

This study also promotes a method of simulation for the model
that is likely to provide the probabilistic forecasting of crisis occurrence.
By bootstrapping the data input, we can obtain distributions of exchange rates,
interest rates, international reserves and as the result, the exchange market
pressure index. The ability to forecast the probability of crisis in a
non-parametric way given various forecasting horizons is a good starting point
for the rest of the analysis.

It is worth noting that we enrich the EWS literature by using
vector autoregressive models with three components which are exchange rates,
interest rates, international reserves. These time series are available to be
accessed at monthly frequency which means that the model does not need to
convert from low-frequency data (quarterly and annually) to higher-frequency
data. This feature is quite appealing
when we note that the conventional EWSs often use fundamentals as the main
inputs. Clearly, such models lacks precision on two counts; they distort the
data by adopting different methods to transform data to higher frequencies
because the fundamental variables are usually available at either quarter and
annual frequencies, and they do not cover a long research time period and
include many countries in the sample because a large number of fundamental
variables are just available recently.

In the same vein, using only three well-updated variables as
the data inputs for the model helps us come close to the real-time forecasting
which are impossible in the case of the EWSs with many dependent variables. In
the real world, macroeconomic data often has lag time to be published
officially. To put this another way, the conventional EWSs which rely too much
on a wide range of data that can not provide an in-time prediction for crises.
As a result, the governments are unlikely to be warned before the crises occur
and can not give out defensive policy in a timely manner.

In this research, we have developed a new approach for the
Early Warning System of currency crises by first predicting three components of
a crisis definition and then establishing the crisis signal given these
predicted components. The conventional Early Warning Systems use the discrete
crisis variable as the dependent variable. We also predict each separate time
point (month in our study) as crisis or non-crisis while the existing models
bundle all time points in a certain period of time as a crisis. According to
this approach, either the policy makers or investors can have predictions of
crisis of every month rather than only a probability of crisis for a specific
period.

This study achieves important findings given the analysis on
both in-sample and out-of-sample. Firstly, through 5 main performance measures,
the ROC curves and AUC values, both model 1 and model 2 have powerful performance
especially when applied to the KLR and KLR-revised crisis definitions. For the
case of Zhang's crisis definition, although the relative amounts of false
alarms is rather high, the other remaining measures such as the AUC values show
good results. Secondly, the out-of-sample performance of our models is even
substantially better than the in-sample performance which is a plus point when
we aim at real-time forecasting.