Stochastic Variational Inference for GARCH Models

Hanwen Xuan, Luca Maestrini, Feng Chen and Clara Grazian

Papers from arXiv.org

Abstract: Stochastic variational inference algorithms are derived for fitting various heteroskedastic time series models. We examine Gaussian, t, and skew-t response GARCH models and fit these using Gaussian variational approximating densities. We implement efficient stochastic gradient ascent procedures based on the use of control variates or the reparameterization trick and demonstrate that the proposed implementations provide a fast and accurate alternative to Markov chain Monte Carlo sampling. Additionally, we present sequential updating versions of our variational algorithms, which are suitable for efficient portfolio construction and dynamic asset allocation.

Date: 2023-08
New Economics Papers: this item is included in nep-ecm and nep-ets
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