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GARMA, HAR and Rules of Thumb for Modelling Realized Volatility

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  • David Edmund Allen

    (School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia
    Department of Finance, Asia University, Taichung 41354, Taiwan
    School of Business and Law, Edith Cowan University, Joondalup, WA 6027, Australia)

  • Shelton Peiris

    (School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia)

Abstract

This paper features an analysis of the relative effectiveness, in terms of the Adjusted R-Square, of a variety of methods of modelling realized volatility (RV), namely the use of Gegenbauer processes in Auto-Regressive Moving Average format, GARMA, as opposed to Heterogenous Auto-Regressive HAR models and simple rules of thumb. The analysis is applied to two data sets that feature the RV of the S&P500 index, as sampled at 5 min intervals, provided by the OxfordMan RV database. The GARMA model does perform slightly better than the HAR model, but both models are matched by a simple rule of thumb regression model based on the application of lags of squared, cubed and quartic, demeaned daily returns.

Suggested Citation

  • David Edmund Allen & Shelton Peiris, 2023. "GARMA, HAR and Rules of Thumb for Modelling Realized Volatility," Risks, MDPI, vol. 11(10), pages 1-15, October.
  • Handle: RePEc:gam:jrisks:v:11:y:2023:i:10:p:179-:d:1260782
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    References listed on IDEAS

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    1. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July.
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    5. David Edmund Allen, 2020. "Stochastic Volatility and GARCH: Do Squared End-of-Day Returns Provide Similar Information?," JRFM, MDPI, vol. 13(9), pages 1-25, September.
    6. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
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