Variational Inference for GARCH-family Models
Martin Magris Alexandros Iosifidis
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.