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Forecasting Value-at-Risk using high frequency data: The realized range model

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Author Info

  • Shao, Xi-Dong
  • Lian, Yu-Jun
  • Yin, Lian-Qian

Abstract

Current studies on financial market risk measures usually use daily returns based on GARCH type models. This paper models realized range using intraday high frequency data based on CARR framework and apply it to VaR forecasting. Kupiec LR test and dynamic quantile test are used to compare the performance of VaR forecasting of realized range model with another intraday realized volatility model and daily GARCH type models. Empirical results of Chinese Stock Indices show that realized range model performs the same with realized volatility model, which performs much better than daily models.

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Bibliographic Info

Article provided by Elsevier in its journal Global Finance Journal.

Volume (Year): 20 (2009)
Issue (Month): 2 ()
Pages: 128-136

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Handle: RePEc:eee:glofin:v:20:y:2009:i:2:p:128-136

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Web page: http://www.elsevier.com/locate/inca/620162

Related research

Keywords: VaR Realized range High frequency data;

References

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  1. Pierre Giot and S»bastien Laurent, 2001. "Value-At-Risk For Long And Short Trading Positions," Computing in Economics and Finance 2001, Society for Computational Economics 94, Society for Computational Economics.
  2. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," NBER Working Papers 8160, National Bureau of Economic Research, Inc.
  3. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 22, pages 367-381, October.
  4. Martens, Martin & van Dijk, Dick, 2007. "Measuring volatility with the realized range," Journal of Econometrics, Elsevier, Elsevier, vol. 138(1), pages 181-207, May.
  5. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, American Finance Association, vol. 57(3), pages 1047-1091, 06.
  6. Chou, Ray Yeutien, 2005. "Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model," Journal of Money, Credit and Banking, Blackwell Publishing, Blackwell Publishing, vol. 37(3), pages 561-82, June.
  7. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, Elsevier, vol. 131(1-2), pages 97-121.
  8. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
  9. GIOT, Pierre & LAURENT, Sébastien, . "Value-at-Risk for long and short trading positions," CORE Discussion Papers RP, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) -1707, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  10. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 95-24, Board of Governors of the Federal Reserve System (U.S.).
  11. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, University of Chicago Press, vol. 53(1), pages 61-65, January.
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Cited by:
  1. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(1), pages 28-42.
  2. Stavros Degiannakis & Pamela Dent & Christos Floros, 2014. "A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification," Manchester School, University of Manchester, University of Manchester, vol. 82(1), pages 71-102, 01.
  3. Liu, Chun & Maheu, John M., 2012. "Intraday dynamics of volatility and duration: Evidence from Chinese stocks," Pacific-Basin Finance Journal, Elsevier, Elsevier, vol. 20(3), pages 329-348.
  4. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
  5. Degiannakis, Stavros & Floros, Christos & Dent, Pamela, 2013. "Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence," International Review of Financial Analysis, Elsevier, Elsevier, vol. 27(C), pages 21-33.
  6. Chun Liu & John M Maheu, 2010. "Intraday Dynamics of Volatility and Duration: Evidence from the Chinese Stock Market," Working Papers, University of Toronto, Department of Economics tecipa-401, University of Toronto, Department of Economics.
  7. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.

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