BUSINESS ANALYTICS WORKING PAPER SERIES Bayesian Semi-parametric Expected Shortfall Forecasting in Financial Markets
نویسندگان
چکیده
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. January 2012 BA Working Paper No: 01/2012 http://sydney.edu.au/business/business_analytics/research/working_papers Bayesian Semi-parametric Expected Shortfall Forecasting in Financial Markets Richard H. Gerlach Discipline of Business Analytics, University of Sydney, Australia. ([email protected]) Cathy W. S. Chen∗ Department of Statistics, Feng Chia University, Taiwan. ([email protected]) Liou-Yan Lin Department of Statistics, Feng Chia University, Taiwan. ([email protected]) ∗Corresponding author is Cathy W. S. Chen. Email: [email protected].
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