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Stochastic Variational Inference for GARCH Models

Author

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  • Hanwen Xuan
  • Luca Maestrini
  • Feng Chen
  • Clara Grazian

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.

Suggested Citation

  • Hanwen Xuan & Luca Maestrini & Feng Chen & Clara Grazian, 2023. "Stochastic Variational Inference for GARCH Models," Papers 2308.14952, arXiv.org.
    • Handle: RePEc:arx:papers:2308.14952
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