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Semi-parametric Expected Shortfall Forecasting

Cathy W. S. Chen () and Richard Gerlach

No 2014_02, Working Papers from University of Sydney Business School, Discipline of Business Analytics

Abstract: Intra-day sources of data have proven effective for dynamic volatility and tail risk estimation. Expected shortfall is a tail risk measure, that is now recommended by the Basel Committee, involving a conditional expectation that can be semi-parametrically estimated via an asymmetric sum of squares function. The conditional autoregressive expectile class of model, used to indirectly model expected shortfall, is generalised to incorporate information on the intra-day range. An asymmetric Gaussian density model error formulation allows a likelihood to be developed that leads to semiparametric estimation and forecasts of expectiles, and subsequently of expected shortfall. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation, while their performance is assessed via a simulation study. The proposed models compare favourably with a large range of competitors in an empirical study forecasting seven financial return series over a ten year perio d.

Keywords: CARE model; Nonlinear; Asymmetric Gaussian distribution; Expected; Markov chain Monte Carlo method; Semi-parametric (search for similar items in EconPapers)
Date: 2014-04
New Economics Papers: this item is included in nep-ecm, nep-for and nep-rmg
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