AdaVol: An Adaptive Recursive Volatility Prediction Method

Nicklas Werge () and Olivier Wintenberger
Additional contact information
Nicklas Werge: LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité

Post-Print from HAL

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, the QML estimation in streaming settings has attracted little attention 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.

Keywords: recursive algorithm; quasi-likelihood; volatility models; GARCH; prediction method; stock index (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-dem and nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-02733439v3
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Econometrics and Statistics , 2022, 23, pp.2452-3062. ⟨10.1016/j.ecosta.2021.01.004⟩

Downloads: (external link)
https://hal.science/hal-02733439v3/document (application/pdf)

Related works:
Journal Article: AdaVol: An Adaptive Recursive Volatility Prediction Method (2022)
Working Paper: AdaVol: An Adaptive Recursive Volatility Prediction Method (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02733439

DOI: 10.1016/j.ecosta.2021.01.004

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().