Skew selection for factor stochastic volatility models

Jouchi Nakajima

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arXiv · 1903.11005

Skew selection for factor stochastic volatility models

Jouchi Nakajima

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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.

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Jouchi Nakajima. 2019-03-26. Skew selection for factor stochastic volatility models. https://arxiv.org/abs/1903.11005

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