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Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction

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Author Info

  • Fuertes, Ana-Maria
  • Olmo, Jose

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.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 29 (2013)
Issue (Month): 1 ()
Pages: 28-42

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Handle: RePEc:eee:intfor:v:29:y:2013:i:1:p:28-42

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Quantile regression; Optimal forecast combination; Encompassing; Conditional coverage; High-frequency data; Realized variance;

References

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Cited by:
  1. Ahoniemi, Katja & Lanne, Markku, 2013. "Overnight stock returns and realized volatility," International Journal of Forecasting, Elsevier, vol. 29(4), pages 592-604.

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