svcommon-package: svcommon: Fast Inference for Common-Factor Stochastic...

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

svcommon: Fast Inference for Common-Factor Stochastic Volatility Models

Description

Provides various tools for estimating the parameters of the common-factor multivariate stochastic volatility model of Fang et al (2020) \Sexpr[results=rd]{tools:::Rd_expr_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.

Author(s)

Maintainer: Martin Lysy [e-mail]

See Also

Useful links:

• https://github.com/mlysy/svcommon

• https://mlysy.github.io/svcommon/

• Report bugs at https://github.com/mlysy/svcommon/issues

mlysy/svcommon documentation built on Sept. 15, 2024, 1:15 a.m.