具有时变偏度的随机波动率均值向量自回归模型
Stochastic Volatility-in-mean VARs with Time-Varying Skewness
AI总结：
本文提出一种允许波动率和偏度直接影响宏观变量的贝叶斯向量自回归模型，实证发现偏度冲击影响显著且模型预测更优、尾部风险度量更精确。
摘要：
本文提出了一种具有随机波动率均值和时变偏度的贝叶斯向量自回归（BVAR）模型。与以往方法不同，该模型允许波动率和偏度直接对宏观经济变量产生影响。我们提供了一种用于后验推断的吉布斯采样算法，并将该模型应用于美国和英国的季度数据。实证结果表明，偏度冲击对产出、通货膨胀和利差具有经济上显著的影响，其影响往往超过波动率冲击的影响。在伪实时预测练习中，所提出的模型在许多情况下优于现有的替代模型。此外，该模型产生了更精确的尾部风险度量，揭示出标准随机波动率模型往往高估不确定性。这些发现强调了纳入时变偏度对于捕捉宏观金融风险和改善预测性能的重要性。
英文摘要：
This paper introduces a Bayesian vector autoregression (BVAR) with stochastic volatility-in-mean and time-varying skewness. Unlike previous approaches, the proposed model allows both volatility and skewness to directly affect macroeconomic variables. We provide a Gibbs sampling algorithm for posterior inference and apply the model to quarterly data for the US and the UK. Empirical results show that skewness shocks have economically significant effects on output, inflation and spreads, often exceeding the impact of volatility shocks. In a pseudo-real-time forecasting exercise, the proposed model outperforms existing alternatives in many cases. Moreover, the model produces sharper measures of tail risk, revealing that standard stochastic volatility models tend to overstate uncertainty. These findings highlight the importance of incorporating time-varying skewness for capturing macro-financial risks and improving forecast performance.
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