Narrow Results

This study aims to develop an early detection model for the currency crisis in Singapore using a neural network (NN) algorithm based on macroeconomic indicators. By utilizing the exchange market pressure (EMP) threshold approach, this study identifies currency crisis signals that can function as early warnings. In this study, a comparison of four NN optimization methods was conducted, namely stochastic gradient descent (SGD), Adam, Nadam, and AdaBound. The data used include 11 macroeconomic indicators from January 1990 to June 2021. The study results show that the NN model with Nadam optimization provides the best performance, with higher accuracy, sensitivity, and specificity compared to other optimization methods. This model successfully predicted crisis signals with an accuracy of 95.89%, a sensitivity of 98.36%, and a specificity of 83.33%. These findings can be used as a basis for decision-making in anticipating the currency crisis in Singapore.

The key objective of this study is to bring into light several shortcomings of early literatures in identifying episodes of currency crises. A careful examination of the basic statistical distribution of exchange market pressure index, based on a weighting scheme proposed by Eichengreen–Rose–Wyplosz (1995, 1996), reveals that the conventional method of defining currency crisis is statistically flawed. This study applies an alternative statistical method known as Extreme Value Analysis (EVA), originally developed by Hill (1975), and, more recently, extended by Huisman et al. (2001) to the case of Singapore from 1985 to 2003.

This paper examines the extent to which the Indonesia's currency crisis can be accounted for by macro and micro economic fundamentals by employing Markov-switching approach under cross-generation crisis models. In order to represent the speculative attack in the economy, the study utilized one of the measures that is most widely adopted to signal the breakup of a crisis, the Exchange Market Pressure Index (EMPI). This paper found the following. First, liquidity (DC), real exchange rate (RER2) and ratio of banking credit to GDP (BCred) were found to significantly influence the EMPI, indicating that the behavior of EMPI has the characteristic that is predicted by the first, second, and third generation of crisis model found to significantly influence the EMPI, indicating that the behavior of EMPI has the characteristic that is predicted by the first, second and third generation of crisis models. Second, the LR test showed that regime switching dynamic model is more robust than ordinary dynamic model in explaining the EMPI, suggesting that speculative attacks tend to have the characteristics of multiple equilibria. Third, the transition probability matrix results showed that the tranquility regime was more persistent than the volatile regime.

This paper investigates ways of identifying and predicting currency crises in world-wide markets, with special focus on 1997 and 2008 currency crises. A novel Markov switching method is proposed for identifying currency crisis based on two states model, the turmoil state and tranquil state, which is the most suitable model considering the balance between model performance and computational demand. Compared with previous Markov switching currency crisis studies, the contribution of this paper comes from several ways. First, the dependent variable is different. While other papers use the exchange rate directly or the estimation of devaluation probability, this study uses the market pressure index calculated from nominal exchange rate and foreign reserves. Secondly, we allow different volatilities in different states, whereas other papers assume the same volatility in two states. Thirdly, our transition probabilities are constant rather than time-varying. The model shows evidence of state switching before crisis in many different currency markets. Lastly, we compare the Markov switching method with the widely used probit model which proposed an early warning system in terms of forecasting performance, and the empirical results show that the novel Markov switching method performs better than the probit model.

Are there early warnings of an impending financial crisis in China? Our analysis using the Kaminsky–Lizondo–Reinhart (KLR) signal approach reveals that the probability of China having a currency crisis in the 24 months to October 2017 could be increased assuming no remedial action by the authorities to avert an impending crisis. Notwithstanding the above, our analysis shows that nine out of 15 economic indicators are effective in predicting a currency crisis. Loss function of policymakers and evaluation of usefulness are then employed to verify their validity. The results show that bank deposits and M2/international reserves are the most powerful indicators.