Bayesian Expected Shortfall Forecasting Incorporating the Intraday Range

Richard Gerlach and Cathy W. S. Chen ()

Journal of Financial Econometrics, 2016, vol. 14, issue 1, 128-158

Abstract: Intraday sources of data have proved to be effective for dynamic volatility and tail risk estimation. Expected shortfall (ES) is a tail risk measure, which 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 implicitly model ES, is generalized to incorporate information on the intraday range. An asymmetric Gaussian density model error formulation allows a likelihood to be developed that leads to direct estimation and one-step-ahead forecasts of expectiles and, subsequently, of ES. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation, while their performance is assessed via a simulation study. The proposed models compare favorably with a large range of competitors in an empirical study forecasting seven financial return series over a 10-year period.

Keywords: asymmetric Gaussian distribution; CARE model; expected shortfall; Markov chain Monte Carlo method; nonlinear (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://hdl.handle.net/10.1093/jjfinec/nbu022 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:jfinec:v:14:y:2016:i:1:p:128-158.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Journal of Financial Econometrics is currently edited by Allan Timmermann and Fabio Trojani

More articles in Journal of Financial Econometrics from Oxford University Press Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().