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

Kyong Joo Oh, Tae Yoon Kim, Chiho Kim

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)83-98
Number of pages16
JournalExpert Systems
Issue number2
Publication statusPublished - 2006 Jun

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence


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