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Quantile Forecasts of Financial Returns Using Realized GARCH Models

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  • Toshiaki Watanabe

Abstract

This article applies the realized GARCH model, which incorporates the GARCH model with realized volatility (RV), to quantile forecasts of financial returns such as Value-at-Risk and expected shortfall. This model has certain advantages in the application to quantile forecasts because it can adjust the bias of RV casued by microstructure noise and non-trading hours and enables us to estimate the parameters in the return distribution jointly with the other parameters. Student's t- and skewed strudent's t-distributions as well as normal distribution are used for the return distribution. The EGARCH model is used for comparison. Main results for the S&P 500 stock index are: (1) the realized GARCH model with the skewed student's t-distribution performs better than that with the normal and student's t-distributions and the EGARCH model using the daily returns only, and (2) the performance does not improve if the realized kernel, which takes account of microstructure noise, is used instead of the plain realized volatility, implying that the realized GARCH model can adjust the bias of RV caused by microstructure noise.

Suggested Citation

  • Toshiaki Watanabe, 2011. "Quantile Forecasts of Financial Returns Using Realized GARCH Models," Global COE Hi-Stat Discussion Paper Series gd11-195, Institute of Economic Research, Hitotsubashi University.
  • Handle: RePEc:hst:ghsdps:gd11-195
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    File URL: http://gcoe.ier.hit-u.ac.jp/research/discussion/2008/pdf/gd11-195.pdf
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    1. Alan B. Krueger, 1997. "Experimental Estimates of Education Production Functions," Working Papers 758, Princeton University, Department of Economics, Industrial Relations Section..
    2. Gary-Bobo, Robert J. & Mahjoub, Mohamed Badrane, 2006. "Estimation of Class-Size Effects, Using 'Maimonides' Rule': The Case of French Junior High Schools," CEPR Discussion Papers 5754, C.E.P.R. Discussion Papers.
    3. Wo[ss]mann, Ludger & West, Martin, 2006. "Class-size effects in school systems around the world: Evidence from between-grade variation in TIMSS," European Economic Review, Elsevier, vol. 50(3), pages 695-736, April.
    4. Takashi Oshio & Wataru Seno, 2007. "The Economics of Education in Japan: A Survey of Empirical Studies and Unresolved Issues," Japanese Economy, Taylor & Francis Journals, pages 46-81.
    5. Eskil Heinesen, 2010. "Estimating Class-size Effects using Within-school Variation in Subject-specific Classes," Economic Journal, Royal Economic Society, vol. 120(545), pages 737-760, June.
    6. Datar, Ashlesha & Mason, Bryce, 2008. "Do reductions in class size "crowd out" parental investment in education?," Economics of Education Review, Elsevier, vol. 27(6), pages 712-723, December.
    7. Miguel Urquiola & Eric Verhoogen, 2009. "Class-Size Caps, Sorting, and the Regression-Discontinuity Design," American Economic Review, American Economic Association, pages 179-215.
    8. Miguel Urquiola & Eric Verhoogen, 2009. "Class-Size Caps, Sorting, and the Regression-Discontinuity Design," American Economic Review, American Economic Association, pages 179-215.
    9. Ludger Woesmann, 2003. "Schooling Resources, Educational Institutions and Student Performance: the International Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, pages 117-170.
    10. Caroline M. Hoxby, 2000. "The Effects of Class Size on Student Achievement: New Evidence from Population Variation," The Quarterly Journal of Economics, Oxford University Press, vol. 115(4), pages 1239-1285.
    11. Fertig, Michael, 2003. "Educational Production, Endogenous Peer Group Formation and Class Composition - Evidence from the PISA 2000 Study," Royal Economic Society Annual Conference 2003 76, Royal Economic Society.
    12. Edwin Leuven & Hessel Oosterbeek & Marte Rønning, 2008. "Quasi-experimental Estimates of the Effect of Class Size on Achievement in Norway," Scandinavian Journal of Economics, Wiley Blackwell, pages 663-693.
    13. Kelly Bedard & Elizabeth Dhuey, 2006. "The Persistence of Early Childhood Maturity: International Evidence of Long-Run Age Effects," The Quarterly Journal of Economics, Oxford University Press, vol. 121(4), pages 1437-1472.
    14. Alan B. Krueger, 1999. "Experimental Estimates of Education Production Functions," The Quarterly Journal of Economics, Oxford University Press, vol. 114(2), pages 497-532.
    15. Miguel Urquiola, 2006. "Identifying Class Size Effects in Developing Countries: Evidence from Rural Bolivia," The Review of Economics and Statistics, MIT Press, pages 171-177.
    16. Hans Bonesrønning, 2003. "Class Size Effects on Student Achievement in Norway: Patterns and Explanations," Southern Economic Journal, Southern Economic Association, vol. 69(4), pages 952-965, April.
    17. Fertig, Michael, 2003. "Educational Production, Endogenous Peer Group Formation and Class Composition - Evidence from the PISA 2000 Study," Royal Economic Society Annual Conference 2003 76, Royal Economic Society.
    18. Martin Browning & Eskil Heinesen, 2003. "Class size, teacher hours and educational attainment," CAM Working Papers 2003-15, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics, revised Jun 2005.
    19. Torbjørn Hægeland & Oddbjørn Raaum & Kjell G. Salvanes, 2007. "Pennies from heaven. Using exogenous tax variation to identify effects of school resources on pupil achievement," Discussion Papers 508, Statistics Norway, Research Department.
    20. Andrea M. Mühlenweg & Patrick A. Puhani, 2010. "The Evolution of the School-Entry Age Effect in a School Tracking System," Journal of Human Resources, University of Wisconsin Press.
    21. Hojo, Masakazu & Oshio, Takashi, 2010. "What factors determine student performance in East Asia? New evidence from TIMSS 2007," PIE/CIS Discussion Paper 494, Center for Intergenerational Studies, Institute of Economic Research, Hitotsubashi University.
    22. Christopher Jepsen & Steven Rivkin, 2009. "Class Size Reduction and Student Achievement: The Potential Tradeoff between Teacher Quality and Class Size," Journal of Human Resources, University of Wisconsin Press.
    23. Hægeland, Torbjørn & Raaum, Oddbjørn & Salvanes, Kjell G., 2012. "Pennies from heaven? Using exogenous tax variation to identify effects of school resources on pupil achievement," Economics of Education Review, Elsevier, vol. 31(5), pages 601-614.
    24. Martin Browning & Eskil Heinesen, 2007. "Class Size, Teacher Hours and Educational Attainment," Scandinavian Journal of Economics, Wiley Blackwell, pages 415-438.
    25. Kawaguchi, Daiji, 2011. "Actual age at school entry, educational outcomes, and earnings," Journal of the Japanese and International Economies, Elsevier, pages 64-80.
    26. Joshua D. Angrist & Victor Lavy, 1999. "Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement," The Quarterly Journal of Economics, Oxford University Press, vol. 114(2), pages 533-575.
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    More about this item

    Keywords

    Expected shortfall; GARCH; Realized volatility; Skewed student's t-distribution; Value-at-Risk;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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