AdaVol: An Adaptive Recursive Volatility Prediction Method

TL;DR

AdaVol introduces an adaptive recursive method for GARCH volatility prediction that combines stochastic approximation with variance targeting, offering computational efficiency and improved stability in streaming data scenarios.

Contribution

The paper develops AdaVol, a novel recursive GARCH estimation method that extends QML procedures to streaming data, addressing convergence and computational challenges.

Findings

AdaVol achieves stable and adaptive volatility estimates in real-time data.

The method demonstrates computational efficiency suitable for large-scale problems.

Empirical results show improved convergence and adaptability over traditional methods.

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.