Bayesian time-varying quantile forecasting for Value-at-Risk in financial markets

Nancy Y. C. Chan, Cathy W. S. Chen () and Richard Gerlach

No 9 OMEWP, Working Papers from University of Sydney Business School, Discipline of Business Analytics

Abstract: Recently, Bayesian solutions to the quantile regression problem, via the likelihood of a Skewed-Laplace distribution, have been proposed. These approaches are extended and applied to a family of dynamic conditional autoregressive quantile models. Popular Value at Risk models, used for risk management in finance, are extended to this fully nonlinear family. An adaptive Markov chain Monte Carlo sampling scheme is adapted for estimation and inference. Simulation studies illustrate favourable performance, compared to the standard numerical optimization of the usual nonparametric quantile criterion function, in finite samples. An empirical study generating Value at Risk forecasts for ten major financial stock indices finds significant nonlinearity in dynamic quantiles and evidence favoring the proposed model family, for lower level quantiles, compared to a range of standard parametric volatility models, a semi-parametric smoothly mixing regression and some nonparametric risk measures, in the literature.

Keywords: CAViaR model; Asymmetric; Skew-Laplace distribution; Value-at-Risk; GARCH; Regression quantile (search for similar items in EconPapers)
Date: 2009-08
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http://hdl.handle.net/2123/8159

Related works:
Journal Article: Bayesian Time-Varying Quantile Forecasting for Value-at-Risk in Financial Markets (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:syb:wpbsba:2123/8159

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