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

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Listed author(s):
  • 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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File URL: http://www.sciencedirect.com/science/article/pii/S0169207012000805
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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
DOI: 10.1016/j.ijforecast.2012.05.005
Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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

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Cited by:

  1. repec:eee:intfor:v:33:y:2017:i:3:p:662-678 is not listed on IDEAS
  2. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
  3. repec:eee:finana:v:51:y:2017:i:c:p:25-53 is not listed on IDEAS
  4. Ana-Maria Fuertes & Elena Kalotychou & Natasa Todorovic, 2015. "Daily volume, intraday and overnight returns for volatility prediction: profitability or accuracy?," Review of Quantitative Finance and Accounting, Springer, vol. 45(2), pages 251-278, August.
  5. Chiu, Yen-Chen & Chuang, I-Yuan, 2016. "The performance of the switching forecast model of value-at-risk in the Asian stock markets," Finance Research Letters, Elsevier, vol. 18(C), pages 43-51.
  6. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
  7. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 9(3), pages 1-20, September.
  8. 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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