论文标题
Adavol：一种自适应递归波动性预测方法
AdaVol: An Adaptive Recursive Volatility Prediction Method
论文作者
论文摘要
准最大可能性（QML）程序在理论上具有吸引力，并广泛用于统计推断。尽管在批处理设置中有广泛的QML估计参考文献，但直到最近，它在流媒体设置中几乎没有引起关注。进行了对QML程序的收敛性能在一般有条件异质的时间序列模型中的调查，并且经典的批次优化例程扩展到了流和大规模问题的框架。提出了名为Adavol的Garch模型的自适应递归估计程序。 ADAVOL程序依赖于随机近似结合了方差靶向估计技术（VTE）。这种递归方法具有计算有效的属性，而VTE减轻了由于缺乏凸度而导致的QML估计遇到的一些收敛困难。经验结果表明，Adavol的稳定性与适应现实生活数据的时变估计的能力之间有一个有利的权衡。
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.
