Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis
AbstractValue-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus standard and nonlinear stochastic volatility models, most considering four error probability distributions: Gaussian, Student-t, skewed-t and generalized error distribution. Adaptive Markov chain Monte Carlo methods are employed in estimation and forecasting. A portfolio of four Asia-Pacific stock markets is considered. Two forecasting periods are evaluated in light of the recent global financial crisis. Results reveal that: (i) GARCH models out-performed stochastic volatility models in almost all cases; (ii) asymmetric volatility models were clearly favoured pre-crisis; while at the 1% level during and post-crisis, for a 1 day horizon, models with skewed-t errors ranked best, while IGARCH models were favoured at the 5% level; (iii) all models forecasted VaR less accurately and anti-conservatively post-crisis
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 31 (2012)
Issue (Month): 8 (December)
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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
Other versions of this item:
- Chen, Cathy W.S & Gerlach, Richard & Lee, Wcw & Lin, Edward M.H., 2011. "Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis," Working Papers 1 OMEWP, University of Sydney Business School, Discipline of Business Analytics.
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- Chen, C.W.S. & Gerlach, R. & Hwang, B.B.K. & McAleer, M.J., 2011.
"Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intraday Range,"
Econometric Institute Research Papers
EI 2011-17, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael, 2012. "Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range," International Journal of Forecasting, Elsevier, vol. 28(3), pages 557-574.
- Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," Working Papers in Economics 11/22, University of Canterbury, Department of Economics and Finance.
- Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," KIER Working Papers 775, Kyoto University, Institute of Economic Research.
- Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," Documentos del Instituto Complutense de AnÃ¡lisis EconÃ³mico 2011-16, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
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