Skew selection for factor stochastic volatility models

Jouchi Nakajima

Journal of Applied Statistics, 2020, vol. 47, issue 4, 582-601

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.

Date: 2020
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DOI: 10.1080/02664763.2019.1646227

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