Skew selection for factor stochastic volatility models
Methodology
2019-03-27 v1
Abstract
This paper proposes factor stochastic volatility models with skew error distributions. The generalized hyperbolic skew t-distribution is employed for common-factor processes and idiosyncratic shocks. Using a Bayesian sparsity modeling strategy for the skewness parameter provides a parsimonious skew structure for possibly high-dimensional stochastic volatility models. Analyses of daily stock returns are provided. Empirical results show that the skewness is important for common-factor processes but less for idiosyncratic shocks. The sparse skew structure improves prediction and portfolio performance.
Cite
@article{arxiv.1903.11005,
title = {Skew selection for factor stochastic volatility models},
author = {Jouchi Nakajima},
journal= {arXiv preprint arXiv:1903.11005},
year = {2019}
}