Variational Inference for GARCH-family Models

• URL: http://arxiv.org/abs/2310.03435v1
• Date: Thu, 5 Oct 2023 10:21:31 GMT
• Title: Variational Inference for GARCH-family Models
• Authors: Martin Magris, Alexandros Iosifidis
• Abstract summary: Variational Inference is a robust approach for Bayesian inference in machine learning models. We show that Variational Inference is an attractive, remarkably well-calibrated, and competitive method for Bayesian learning.
• Score: 84.84082555964086
• License: http://creativecommons.org/licenses/by/4.0/
• 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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