TY - JOUR
T1 - Time series analysis for financial market meltdowns
AU - Kim, Young Shin
AU - Rachev, Svetlozar T.
AU - Bianchi, Michele Leonardo
AU - Mitov, Ivan
AU - Fabozzi, Frank J.
N1 - Funding Information:
Rachev gratefully acknowledges research support through grants from the Division of Mathematical, Life and Physical Sciences, College of Letters and Science, University of California, Santa Barbara (where he is Professor Emeritus); the Deutschen Forschungsgemeinschaft; and the Deutscher Akademischer Austausch Dienst. Bianchi acknowledges that the views expressed in this paper are his alone and should not be attributed to those of his employer. We thank the referee of this paper for providing numerous valuable comments that helped to improve the paper.
PY - 2011/8
Y1 - 2011/8
N2 - 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.
AB - 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.
KW - ARMA-GARCH model
KW - Average value-at-risk (AVaR)
KW - Tempered stable distribution
KW - Value-at-risk (VaR)
KW - α-stable distribution
UR - http://www.scopus.com/inward/record.url?scp=79957605668&partnerID=8YFLogxK
U2 - 10.1016/j.jbankfin.2010.12.007
DO - 10.1016/j.jbankfin.2010.12.007
M3 - Article
AN - SCOPUS:79957605668
VL - 35
SP - 1879
EP - 1891
JO - Journal of Banking and Finance
JF - Journal of Banking and Finance
SN - 0378-4266
IS - 8
ER -