 Bayesian Semi-parametric Expected Shortfall Forecasting in Financial M arketsCathy W. S. Chen (), Richard Gerlach and Liou-Yan Lin No 12 BAWP, Working Papers from University of Sydney Business School, Discipline of Business Analytics Abstract: Bayesian semi-parametric estimation has proven effective for quantile estimation in general and specifically in financial Value at Risk forecasting. Expected short-fall is a competing tail risk measure, involving a conditional expectation beyond a quantile, that has recently been semi-parametrically estimated via asymmetric least squares and so-called expectiles. An asymmetric Gaussian density is proposed allowing a likelihood to be developed that leads to Bayesian semi-parametric estimation and forecasts of expectiles and expected shortfall. Further, the conditional autoregressive expectile class of model is generalised to two fully nonlinear families. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation in these families. The proposed models are clearly favoured in an empirical study forecasting eleven financial return series: clear evidence of more accurate expected shortfall forecasting, compared to a range of competing methods is found. Further, the most favoured models are those estimated by Bayesian methods. Keywords: CARE model; Nonlinear; Asymmetric Gaussian distribution; Expected shortfall; semi-parametric (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:syb:wpbsba:2123/8169 Access Statistics for this paper More papers in Working Papers from University of Sydney Business School, Discipline of Business Analytics Contact information at EDIRC. |
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