IDENTIFICATION AND PREDICTION OF CURRENCY CRISIS: MARKOV SWITCHING-BASED APPROACH

Jiangze Du (), Runfang Yu and Kin Keung Lai
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Jiangze Du: School of Finance, Jiangxi University of Finance and Economics, P. R. China
Runfang Yu: #x2020;Hong Kong Applied Science and Technology Research Institute, Hong Kong
Kin Keung Lai: #x2021;School of Economics and Management, Xidian University, P. R. China§Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong

The Singapore Economic Review (SER), 2020, vol. 65, issue 06, 1667-1698

Abstract: 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.

Keywords: Markov switching; probit model; currency crisis (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0217590818500029

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