Nakajima Jouchi
Bank for International Settlements, Basel, Switzerland.
J Appl Stat. 2019 Jul 24;47(4):582-601. doi: 10.1080/02664763.2019.1646227. eCollection 2020.
This paper proposes factor stochastic volatility models with skew error distributions. The generalized hyperbolic skew -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.
本文提出了具有偏态误差分布的因子随机波动率模型。广义双曲线偏态分布用于共同因子过程和特质冲击。对偏度参数采用贝叶斯稀疏建模策略，为可能的高维随机波动率模型提供了简洁的偏态结构。文中给出了对每日股票收益的分析。实证结果表明，偏度对共同因子过程很重要，但对特质冲击不太重要。稀疏偏态结构改善了预测和投资组合表现。
