Title: Stochastic variational inference for heteroskedastic time series models
Authors: Hanwen Xuan - The University of New South Wales (Australia) [presenting]
Luca Maestrini - The Australian National University (Australia)
Clara Grazian - University of Sydney (Australia)
Feng Chen - UNSW Syd (Australia)
Abstract: Stochastic variational inference algorithms are derived for fitting various heteroskedastic time series models using Gaussian approximating densities. Gaussian, t and skew-t response GARCH models are examined. We implement an efficient stochastic gradient ascent approach based upon the use of control variates or the reparameterization trick and show that the proposed approach offers a fast and accurate alternative to Markov chain Monte Carlo sampling. We also present a sequential updating implementation of our variational algorithms, which is suitable for the construction of an efficient portfolio optimization strategy.