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Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis

Author

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  • Cathy W.S. Chen
  • Richard Gerlach
  • Edward M. H. Lin
  • W. C. W. Lee

Abstract

Value-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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Suggested Citation

  • Cathy W.S. Chen & Richard Gerlach & Edward M. H. Lin & W. C. W. Lee, 2012. "Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(8), pages 661-687, December.
  • Handle: RePEc:wly:jforec:v:31:y:2012:i:8:p:661-687
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    Cited by:

    1. 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.
    2. Chang Liu & Raja Nassar & Min Guo, 2015. "A Method of Retail Mortgage Stress Testing: Based on Time‐Frame and Magnitude Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(4), pages 261-274, July.
    3. Chi Ming Wong & Lei Lam Olivia Ting, 2016. "A Quantile Regression Approach to the Multiple Period Value at Risk Estimation," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 12(1), pages 1-35, February.
    4. Wilson Ye Chen & Richard H. Gerlach, 2017. "Semiparametric GARCH via Bayesian model averaging," Papers 1708.07587, arXiv.org.
    5. repec:eee:empfin:v:42:y:2017:i:c:p:175-198 is not listed on IDEAS
    6. Kim, Minjo & Lee, Sangyeol, 2016. "Nonlinear expectile regression with application to Value-at-Risk and expected shortfall estimation," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 1-19.
    7. repec:ibn:ibrjnl:v:10:y:2017:i:11:p:88-102 is not listed on IDEAS
    8. Chui-Chun Tsai & Tsun-Siou Lee, 2017. "Liquidity-Adjusted Value-at-Risk for TWSE Leverage/ Inverse ETFs: A Hellinger Distance Measure Research," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 13(1), pages 53-81, February.
    9. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    10. Mauro Bernardi & Leopoldo Catania & Lea Petrella, 2014. "Are news important to predict large losses?," Papers 1410.6898, arXiv.org, revised Oct 2014.
    11. Pilar Abad Romero & Sonia Benito Muela & Miguel Angel Sánchez Granero & Carmen López, 2013. "Evaluating the performance of the skewed distributions to forecast Value at Risk in the Global Financial Crisis," Documentos de Trabajo del ICAE 2013-40, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    12. Cathy Chen & Feng-Chi Liu & Mike So, 2013. "Threshold variable selection of asymmetric stochastic volatility models," Computational Statistics, Springer, vol. 28(6), pages 2415-2447, December.

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