Time series analysis for financial market meltdowns

Young Shin Kim, Svetlozar T. Rachev, Michele Leonardo Bianchi, Ivan Mitov, Frank J. Fabozzi

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


There appears to be a consensus that the recent instability in global financial markets may be attributable in part to the failure of financial modeling. More specifically, it is alleged that current risk models have failed to properly assess the risks associated with large adverse stock price behavior. In this paper, we first discuss the limitations of classical time series models for forecasting financial market meltdowns. Then we set forth a framework capable of forecasting both extreme events and highly volatile markets. Based on the empirical evidence presented in this paper, our framework offers an improvement over prevailing models for evaluating stock market risk exposure during distressed market periods.

Original languageEnglish
Pages (from-to)1879-1891
Number of pages13
JournalJournal of Banking and Finance
Issue number8
StatePublished - Aug 2011


  • ARMA-GARCH model
  • Average value-at-risk (AVaR)
  • Tempered stable distribution
  • Value-at-risk (VaR)
  • α-stable distribution


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