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Variational Inference for GARCH-family Models

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  • Martin Magris
  • Alexandros Iosifidis

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.

Suggested Citation

  • Martin Magris & Alexandros Iosifidis, 2023. "Variational Inference for GARCH-family Models," Papers 2310.03435, arXiv.org.
    • Handle: RePEc:arx:papers:2310.03435
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    References listed on IDEAS

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