http://hdl.handle.net/2123/8156
Title: | Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis |
Authors: | Gerlach, Richard Chen, Cathy W.S. Lin, Edward M.H. Lee, Wcw Discipline of Business Analytics |
Keywords: | EGARCH model generalized error distribution Markov chainMonte Carlo method Value-at-Risk Skewed Student-t market risk charge global nancial crisis |
Issue Date: | Mar-2011 |
Publisher: | Business Analytics. |
Series/Report no.: | OMEWP 03/2011 |
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 |
URI: | http://hdl.handle.net/2123/8156 |
Department/Unit/Centre: | Discipline of Business Analytics |
Type of Work: | Working Paper |
Appears in Collections: | Working Papers - Business Analytics |
File | Description | Size | Format | |
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OMWP_2011_03.pdf | 866.97 kB | Adobe PDF |
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