Variational Inference for GARCH-family Models
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.
Cite
@article{arxiv.2310.03435,
title = {Variational Inference for GARCH-family Models},
author = {Martin Magris and Alexandros Iosifidis},
journal= {arXiv preprint arXiv:2310.03435},
year = {2023}
}