A stochastic volatility-in-mean model with correlated errors using the generalized hyperbolic skew Student-t (GHST) distribution provides a robust alternative to the parameter estimation for daily stock returns in the absence of normality. An efficient Markov chain Monte Carlo (MCMC) sampling algorithm is developed for parameter estimation. The deviance information, the Bayesian predictive information and the log-predictive score criterion are used to assess the fit of the proposed model. The proposed method is applied to an analysis of the daily stock return data from the Standard & Poor's 500 index (S&P 500). The empirical results reveal that the stochastic volatility-in-mean model with correlated errors and GH-ST distribution leads to a significant improvement in the goodness-of-fit for the S&P 500 index returns dataset over the usual normal model.

中文翻译：

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使用广义双曲偏斜学生 t 分布对具有杠杆和不对称重尾误差的随机均值波动率模型进行贝叶斯分析

使用广义双曲偏斜 Student-t (GHST) 分布的具有相关误差的随机均值波动率模型为在没有正态性的情况下每日股票收益的参数估计提供了一种可靠的替代方法。为参数估计开发了一种高效的马尔可夫链蒙特卡罗 (MCMC) 采样算法。偏差信息、贝叶斯预测信息和对数预测评分标准用于评估所提出模型的拟合。建议的方法应用于分析标准普尔 500 指数 (S&P 500) 的每日股票收益数据。实证结果表明，具有相关误差和 GH-ST 分布的随机均值波动率模型显着提高了 S& 的拟合优度