Variational Bayes approximation of factor stochastic volatility models

David Gunawan, Robert Kohn and David Nott

International Journal of Forecasting, 2021, vol. 37, issue 4, 1355-1375

Abstract: Estimation and prediction in high dimensional multivariate factor stochastic volatility models is an important and active research area, because such models allow a parsimonious representation of multivariate stochastic volatility. Bayesian inference for factor stochastic volatility models is usually done by Markov chain Monte Carlo methods (often by particle Markov chain Monte Carlo methods), which are usually slow for high dimensional or long time series because of the large number of parameters and latent states involved. Our article makes two contributions. The first is to propose a fast and accurate variational Bayes methods to approximate the posterior distribution of the states and parameters in factor stochastic volatility models. The second is to extend this batch methodology to develop fast sequential variational updates for prediction as new observations arrive. The methods are applied to simulated and real datasets, and shown to produce good approximate inference and prediction compared to the latest particle Markov chain Monte Carlo approaches, but are much faster.

Keywords: Bayesian inference; Prediction; State space model; Stochastic gradient; Sequential variational inference (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:4:p:1355-1375

DOI: 10.1016/j.ijforecast.2021.05.001

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