Titlebook: Early Warning Systems for Financial Crises; Applications to East Asian Development Bank Book 2005 Palgrave Macmillan, a division of Macmill

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über das Problem der Reparationenystems can also easily undermine macroeconomic fundamentals. With increasing globalization and international capital flows, vulnerabilities in one economy can quickly spill over to affect others. Wise policy choices and institutional reforms not only benefit a country itself, but also benefit neighb

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https://doi.org/10.1007/978-3-662-24846-1literature on currency crises begins with models developed to explain crises experienced by some Latin American countries in the late 1970s. These models view currency crises as being caused by weak economic fundamentals. Following the collapse of the European Monetary System in 1992, the so-called

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über Dreharbeit und Werkzeugst?hleses in emerging economies. Investing in the development of an early warning model is important for two reasons. First, banking and currency crises are extremely costly to the countries in which they originate—as well as to other countries that are affected by the spillover of the original crisis.

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https://doi.org/10.1007/978-3-662-33749-3g approach pioneered by Kaminsky and Reinhart (1999). They are estimated using monthly data of six East Asian countries—Indonesia, Republic of Korea (Korea), Malaysia, Philippines, Singapore, and Thailand—and, therefore, may be considered “regional models.” In contrast, empirical EWS models reported

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https://doi.org/10.1007/978-3-662-29580-9 which usually take either a probit or logit approach. Earlier studies on parametric EWS models were mostly based on annual data (see, for example, Frankel and Rose 1996). Such models could be useful in identifying causes of financial crises, but may not be suitable for real-time forecasting of cris

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Nonparametric EWS Models of Currency and Banking Crises for East Asia,Korea), Malaysia, Philippines, Singapore, and Thailand—and, therefore, may be considered “regional models.” In contrast, empirical EWS models reported in existing studies were often estimated using data of 20–30 countries, including both developed and developing countries, and thus can be considered “global models.”

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https://doi.org/10.1007/978-3-662-29580-9is probabilities due to their low frequency. More recently, however, there have been heightened efforts to develop parametric EWS models using monthly data aimed at real-time forecasting. A notable example is the Developing Country Studies Division (DCSD) model of currency crises developed and being used by the International Monetary Fund (IMF).