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arXiv · 2310.03435

Variational Inference for GARCH-family Models

Martin Magris·Alexandros Iosifidis
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Abstract

The Bayesian estimation of GARCH-family models has been typically addressed through Monte Carlo sampling. Variational Inference is gaining popularity and attention as a robust approach for Bayesian inference in complex machine learning models; however, its adoption in econometrics and finance is limited. This paper discusses the extent to which Variational Inference constitutes a reliable and feasible alternative to Monte Carlo sampling for Bayesian inference in GARCH-like models. Through a large-scale experiment involving the constituents of the S&P 500 index, several Variational Inference optimizers, a variety of volatility models, and a case study, we show that Variational Inference is an attractive, remarkably well-calibrated, and competitive method for Bayesian learning.

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BibTeXRIS

Martin Magris, Alexandros Iosifidis. 2023-10-05. Variational Inference for GARCH-family Models. https://arxiv.org/abs/2310.03435

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