|Title:||Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis|
Chen, Cathy W.S.
Lin, Edward M.H.
Discipline of Business Analytics
generalized error distribution
Markov chainMonte Carlo method
market risk charge
global nancial crisis
|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|
|Department/Unit/Centre:||Discipline of Business Analytics|
|Type of Work:||Working Paper|
|Appears in Collections:||Working Papers - Business Analytics|
|OMWP_2011_03.pdf||866.97 kB||Adobe PDF|
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