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AdaVol: An Adaptive Recursive Volatility Prediction Method

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  • Werge, Nicklas
  • Wintenberger, Olivier

Abstract

Quasi-Maximum Likelihood (QML) procedures are theoretically appealing and widely used for statistical inference. While there are extensive references on QML estimation in batch settings, it has attracted little attention in streaming settings until recently. An investigation of the convergence properties of the QML procedure in a general conditionally heteroscedastic time series model is conducted, and the classical batch optimization routines extended to the framework of streaming and large-scale problems. An adaptive recursive estimation routine for GARCH models named AdaVol is presented. The AdaVol procedure relies on stochastic approximations combined with the technique of Variance Targeting Estimation (VTE). This recursive method has computationally efficient properties, while VTE alleviates some convergence difficulties encountered by the usual QML estimation due to a lack of convexity. Empirical results demonstrate a favorable trade-off between AdaVol’s stability and the ability to adapt to time-varying estimates for real-life data.

Suggested Citation

  • Werge, Nicklas & Wintenberger, Olivier, 2022. "AdaVol: An Adaptive Recursive Volatility Prediction Method," Econometrics and Statistics, Elsevier, vol. 23(C), pages 19-35.
    • Handle: RePEc:eee:ecosta:v:23:y:2022:i:c:p:19-35
    DOI: 10.1016/j.ecosta.2021.01.004
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    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    2. Christian Francq & Lajos Horváth, 2011. "Merits and Drawbacks of Variance Targeting in GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 9(4), pages 619-656.
    3. Bougerol, Philippe & Picard, Nico, 1992. "Stationarity of Garch processes and of some nonnegative time series," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 115-127.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Olivier Wintenberger, 2013. "Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 846-867, December.
    6. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    7. Ip, W.C. & Wong, Heung & Pan, J.Z. & Li, D.F., 2006. "The asymptotic convexity of the negative likelihood function of GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 311-331, January.
    8. J. Pfanzagl, 1969. "On the measurability and consistency of minimum contrast estimates," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 14(1), pages 249-272, December.
    9. Nelson, Daniel B., 1990. "Stationarity and Persistence in the GARCH(1,1) Model," Econometric Theory, Cambridge University Press, vol. 6(3), pages 318-334, September.
    10. Sucarrat, Genaro, 2020. "garchx: Flexible and Robust GARCH-X Modelling," MPRA Paper 100301, University Library of Munich, Germany.
    11. 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.
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    Cited by:

    1. Joseph de Vilmarest & Nicklas Werge, 2023. "An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition," Papers 2303.01855, arXiv.org, revised Jun 2024.
    2. Simon Hirsch & Jonathan Berrisch & Florian Ziel, 2024. "Online Distributional Regression," Papers 2407.08750, arXiv.org, revised Aug 2024.

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