Time series analysis for financial market meltdowns
AbstractThere 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Banking & Finance.
Volume (Year): 35 (2011)
Issue (Month): 8 (August)
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Web page: http://www.elsevier.com/locate/jbf
ARMA-GARCH model [alpha]-stable distribution Tempered stable distribution Value-at-risk (VaR) Average value-at-risk (AVaR);
Other versions of this item:
- Young Shin Kim & Rachev, Svetlozar T. & Bianchi, Michele Leonardo & Mitov, Ivan & Fabozzi, Frank J., 2010. "Time series analysis for financial market meltdowns," Working Paper Series in Economics 2, Karlsruhe Institute of Technology (KIT), Department of Economics and Business Engineering.
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