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Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis
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| Other Authors: | Gerlach, Richard H. [Other] • Lin, Edward M.H. [Other] • Wayne [Other] |
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| Type of Resource: | E-Book |
| Language: | English |
| published: |
[S.l.]
SSRN
[2015]
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| Source: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
| Summary: | 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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| Item Description: |
In: Journal of Forecasting, Forthcoming Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 23, 2011 erstellt |
| Physical Description: | 1 Online-Ressource (34 p) |
| DOI: | 10.2139/ssrn.1815603 |
| Access: | Open Access |