AdaVol: An Adaptive Recursive Volatility Prediction Method

Nicklas Werge and Olivier Wintenberger
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Nicklas Werge: LPSM

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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.

Date: 2020-06, Revised 2021-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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http://arxiv.org/pdf/2006.02077 Latest version (application/pdf)

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Journal Article: AdaVol: An Adaptive Recursive Volatility Prediction Method (2022)
Working Paper: AdaVol: An Adaptive Recursive Volatility Prediction Method (2022)
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