City Research Online

Abstract

We make use of quantile regression theory to obtain a combination of individual potentially-biased VaR forecasts that is optimal because, by construction, it meets the correct out-of-sample conditional coverage criterion ex post. This enables a Wald-type conditional quantile forecast encompassing test to be used for any finite set of competing (semi/non)parametric models which can be nested. Two attractive properties of this backtesting approach are its robustness to both model risk and estimation uncertainty. We deploy the techniques to analyse inter-day and high frequency intra-day VaR models for equity, FOREX, fixed income and commodity trading desks. The forecast combination of both types of models is especially warranted for more extreme-tail risks. Overall, our empirical analysis supports the use of high frequency 5 minute price information for daily risk management.

Publication Type:	Article
Additional Information:	NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, Volume 29, Issue 1, January–March 2013, http://dx.doi.org/10.1016/j.ijforecast.2012.05.005
Publisher Keywords:	Quantile regression; Optimal forecast combination; Encompassing; Conditional coverage; High-frequency data; Realized variance
Subjects:	H Social Sciences > HG Finance Q Science > QA Mathematics
Departments:	Bayes Business School > Finance
Related URLs:	Author
SWORD Depositor:	Symplectic Administrator

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