mlysy/svcommon: Fast Inference for Common-Factor Stochastic Volatility Models

Provides various tools for estimating the parameters of the common-factor multivariate stochastic volatility model of Fang et al (2020) <doi:10.1002/cjs.11536> and extensions. In particular, the complete-data likelihood implementation scales linearly in the number of assets, and latent volatilities are efficiently marginalized using the Laplace approximation in the R package 'TMB' with very high accuracy. Combined with a carefully initialized block coordinate descent algorithm, maximum likelihood estimation can be conducted two orders of magnitude faster than with alternative parameter inference algorithms.

install.packages("remotes")
remotes::install_github("mlysy/svcommon")