Markov Chains in Predictive Models of Currency Crises - with Applications to Southeast Asia

• Main Authors: Mariano, Roberto S., Abiad, Abdul G., Bulent, Gultekin, Shabbir, Tayyeb, TAN, Augustine H. H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2002
• Subjects: Asian Studies; Finance; Macroeconomics
• Online Access: https://ink.library.smu.edu.sg/soe_research/765; https://ink.library.smu.edu.sg/context/soe_research/article/1764/viewcontent/RSM_CrisisPaper_WSRCMay02.pdf
• Institution: Singapore Management University

The decade of the 1990s was marked by an unusual number of financial and economic crises such as the attack on the European Monetary System in 1992-93, the Mexican peso crisis in 1994-95, the Asian crisis in 1997, the Russian default in 1998 and its spillover to Latin America. The Turkish currency and banking crisis in 2001 and the recent difficulties in Argentina indicate that financial crises are still part of the current economic events. In the wake of such developments, there has been a resurgence of interest in early warning systems that can anticipate the likely occurrence of such crises. There is an extensive literature on the Asian financial crisis and early warning systems – with numerous surveys including Goldstein (1998), Montes (1998), McLeod and Garnaut (1998), Kaminsky and Reinhart (1998), Tan (1998, 1999), Mariano, Gultekin, Ozmucur and Shabbir (1999), Goldstein, Kaminsky and Reinhart (2000), Richterr (2000), and Abiad (2002). Along these lines, this paper explores the issue of constructing a monthly economic predictive model of currency crises in Southeast Asia through an alternative econometric methodology that addresses drawbacks in existing approaches. Our methodology entails estimating a Markov regime switching model of exchange rate movements, with time-varying transition probabilities. In this paper, we discuss the technical details involved in this approach and apply it to Indonesia, Malaysia, the Philippines and Thailand.