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Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure

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Author Info
Visser, Marcel P.

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Abstract

This paper decomposes volatility proxies according to upward and downward price movements in high-frequency financial data, and uses this decomposition for forecasting volatility. The paper introduces a simple Garch-type discrete time model that incorporates such high-frequency based statistics into a forecast equation for daily volatility. Analysis of S&P 500 index tick data over the years 1988-2006 shows that taking into account the downward movements improves forecast accuracy significantly. The R2 statistic for evaluating daily volatility forecasts attains a value of 0.80, both for in-sample and out-of-sample prediction.

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File URL: http://mpra.ub.uni-muenchen.de/11100/
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Publisher Info
Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 11100.

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Date of creation: 14 Oct 2008
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Handle: RePEc:pra:mprapa:11100

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Related research
Keywords: volatility proxy; downward absolute power variation; log-Garch; volatility asymmetry; leverage effect; SP500; volatility forecasting; high-frequency data;

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Find related papers by JEL classification:
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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References listed on IDEAS
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  1. Tim Bollerslev & Julia Litvinova & George Tauchen, 2006. "Leverage and Volatility Feedback Effects in High-Frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 353-384. [Downloadable!] (restricted)
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  3. Siem Jan Koopman & Borus Jungbacker & Eugenie Hol, 2004. "Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements," Tinbergen Institute Discussion Papers 04-016/4, Tinbergen Institute. [Downloadable!]
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  4. Engle, Robert F & Ng, Victor K, 1993. " Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-78, December. [Downloadable!] (restricted)
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  5. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477. [Downloadable!]
  6. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889. [Downloadable!]
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  7. Taylor, Stephen J., 1987. "Forecasting the volatility of currency exchange rates," International Journal of Forecasting, Elsevier, vol. 3(1), pages 159-170. [Downloadable!] (restricted)
  8. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95. [Downloadable!] (restricted)
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  10. Tim Bollerslev & Jeffrey Wooldridge, 1992. "Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances," Econometric Reviews, Taylor and Francis Journals, vol. 11(2), pages 143-172. [Downloadable!] (restricted)
  11. Brandt, Michael W. & Jones, Christopher S., 2006. "Volatility Forecasting With Range-Based EGARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 470-486, October. [Downloadable!] (restricted)
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  13. Lars Forsberg & Eric Ghysels, 2007. "Why Do Absolute Returns Predict Volatility So Well?," Journal of Financial Econometrics, Oxford University Press, vol. 5(1), pages 31-67. [Downloadable!] (restricted)
  14. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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  15. Visser, Marcel P., 2008. "Ranking and Combining Volatility Proxies for Garch and Stochastic Volatility Models," MPRA Paper 4917, University Library of Munich, Germany. [Downloadable!]
  16. Busch, Thomas, 2005. "A robust LR test for the GARCH model," Economics Letters, Elsevier, vol. 88(3), pages 358-364, September. [Downloadable!] (restricted)
  17. Robert F. Engle & Giampiero M. Gallo, 2003. "A Multiple Indicators Model for Volatility Using Intra-Daily Data," NBER Working Papers 10117, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  18. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March. [Downloadable!] (restricted)
    Other versions:
  19. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 1-37. [Downloadable!] (restricted)
    Other versions:
  20. Visser, Marcel P., 2008. "Garch Parameter Estimation Using High-Frequency Data," MPRA Paper 9076, University Library of Munich, Germany. [Downloadable!]
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