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An early warning system for detection of financial crisis using financial market volatility

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This study proposes an early warning system (EWS) for detection of financial crisis with a daily financial condition indicator (DFCI) designed to monitor the financial markets and provide warning signals. The proposed EWS differs from other commonly used EWSs in two aspects: (i) it is based on dynamic daily movements of the financial markets; and (ii) it is established as a pattern classifier, which identifies predefined unstable states in terms of financial market volatility. Indeed it issues warning signals on a daily basis by judging whether the financial market has entered a predefined unstable state or not. The major strength of a DFCI is that it can issue timely warning signals while other conventional EWSs must wait for the next round input of monthly or quarterly information. Construction of a DFCI consists of two steps where machine learning algorithms are expected to play a significant role, i.e. (i) establishing sub-DFCIs on various daily financial variables by an artificial neural network, and (ii) integrating the sub-DFCIs into an integrated DFCI by a genetic algorithm. The DFCI for the Korean financial market is built as an empirical case study.
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Keywords: artificial neural network; daily financial condition indicator; financial crisis; genetic algorithm; volatility

Document Type: Research Article

Affiliations: 1: Department of Information and Industrial Engineering, Yonsei University, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, South Korea , Email: [email protected] 2: Department of Statistics, Keimyung University, South Korea , Email: [email protected] 3: Korea Deposit Insurance Corporation, South Korea , Email: [email protected]

Publication date: May 1, 2006

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