Variational Inference for GARCH-family Models
This provides a more efficient method for econometricians and financial analysts to perform Bayesian inference on volatility models, though it is incremental as it applies an existing technique to a new domain.
The paper tackled the problem of Bayesian estimation for GARCH-family models in finance, showing that Variational Inference is a reliable and competitive alternative to Monte Carlo sampling, with results indicating it is well-calibrated and effective in large-scale experiments on S&P 500 data.
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.