 Optimally harnessing inter-day and intra-day information for daily value-at-risk predictionAna-Maria Fuertes and Jose Olmo International Journal of Forecasting, 2013, vol. 29, issue 1, 28-42 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. Keywords: Quantile regression; Optimal forecast combination; Encompassing; Conditional coverage; High-frequency data; Realized variance (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:eee:intfor:v:29:y:2013:i:1:p:28-42 DOI: 10.1016/j.ijforecast.2012.05.005 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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