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

Journal of Forecasting, Forthcoming

34 Pages Posted: 21 Apr 2011 Last revised: 27 Jan 2015

See all articles by Cathy W. S. Chen

Cathy W. S. Chen

Feng Chia University - Department of Statistics; Graduate Institute of Statistics & Actuarial Science, Feng Chia University

Richard H. Gerlach

University of Sydney

Edward M.H. Lin

Graduate Institute of Applied Statistics, Feng Chia University

Wayne

Feng Chia University - Graduate Institute of Statistics & Actuarial Science

Date Written: March 23, 2011

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.

Keywords: EGARCH Model, Generalized Error Distribution, Markov Chain Monte Carlo Method, Value-at-Risk, Skewed Student-t, Market Risk Charge, Global Financial Crisis

JEL Classification: C11, C22, C51, C52

Suggested Citation

Chen, Cathy W. S. and Gerlach, Richard H. and Lin, Edward M.H. and Lee, Wayne C.W., Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis (March 23, 2011). Journal of Forecasting, Forthcoming. Available at SSRN: https://ssrn.com/abstract=1815603 or http://dx.doi.org/10.2139/ssrn.1815603

Cathy W. S. Chen (Contact Author)

Feng Chia University - Department of Statistics ( email )

100 Wen Hwa Road
Taichung, 407
Taiwan
886 4 24517250 ext 4412 (Phone)
886 4 24517092 (Fax)

HOME PAGE: http://myweb.fcu.edu.tw/~chenws/

Graduate Institute of Statistics & Actuarial Science, Feng Chia University

100 Wenhwa Road
Talchung
Taiwan
886 4-24517250 ext 4412 (Phone)
886 4-2517092 (Fax)

HOME PAGE: http://myweb.fcu.edu.tw/~chenws/

Richard H. Gerlach

University of Sydney ( email )

Room 483, Building H04
University of Sydney
Sydney, NSW 2006
Australia
+ 612 9351 3944 (Phone)
+ 612 9351 6409 (Fax)

HOME PAGE: http://www.econ.usyd.edu.au/staff/richardg

Edward M.H. Lin

Graduate Institute of Applied Statistics, Feng Chia University ( email )

100 Wen Hwa Road
Taichung, 407
Taiwan

Wayne C.W. Lee

Feng Chia University - Graduate Institute of Statistics & Actuarial Science ( email )

100 Wen Hwa Road
Taichung, 407
Taiwan

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