Application of Fuzzy Optimization and Time Series for Early Warning System in Predicting Currency Crisis

Application of early warning system (EWS) in predicting crisis has drawn a lot of research interests in earlier literature. Recent studies have shown that the development of new EWS models from different field such as artificial intelligence or expert system achieved better prediction than the old statistical model. This paper analyzes the predictability of new methods for EWS which is the combination of time series and fuzzy optimization models. The method used analytic hierarchy process (AHP) to get weights of indicators and ARIMA to forecast the individual indicator and finally by using fuzzy optimization theory to compute the general risk-based relative membership grade. Furthermore, to evaluate the prediction accuracy of our model, we do a comparison of its performance with logistic regression analysis (Logit). According to the results, this model was able to signal currency crisis for four countries only out of ten countries and we concluded that the forecasting power of this model was found to be rather poor. The results emphasized the view that developing a stable model that can predict currency crisis accurately is a challenging task.