Stochastic variational inference for GARCH models

被引：0

作者：

Xuan H. ^{[1
]}

Maestrini L. ^{[2
]}

Chen F. ^{[1
,3
]}

Grazian C. ^{[4
,5
]}

机构：

[1] School of Mathematics and Statistics, University of New South Wales, Anita B. Lawrence Centre, Kensington, 2052, NSW

[2] Research School of Finance, Actuarial Studies and Statistics, The Australian National University, Building 26C, Kingsley Street, Canberra, 2601, ACT

[3] UNSW Data Science Hub, University of New South Wales, Anita B. Lawrence Centre, Kensington, 2052, NSW

[4] School of Mathematics and Statistics, University of Sydney, Carslaw Building, Camperdown, 2006, NSW

[5] ARC Training Centre in Data Analytics for Resources and Environments, University of Sydney, Biomedical Building, South Eveleigh, 2015, NSW

来源：

Statistics and Computing | 2024年 / 34卷 / 1期

关键词：

Bayesian inference; Financial time series; Skewed t innovation; Stochastic gradient descent; Variational Bayes;

D O I：

10.1007/s11222-023-10356-7

中图分类号：

学科分类号：

摘要：

Stochastic variational inference algorithms are derived for fitting various heteroskedastic time series models. We examine Gaussian, t, and skewed t response GARCH models and fit these using Gaussian variational approximating densities. We implement efficient stochastic gradient ascent procedures based on the use of control variates or the reparameterization trick and demonstrate that the proposed implementations provide a fast and accurate alternative to Markov chain Monte Carlo sampling. Additionally, we present sequential updating versions of our variational algorithms, which are suitable for efficient portfolio construction and dynamic asset allocation. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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