标题： 带有时变偏度的随机波动率均值VAR模型 显示英文标题

标题： Stochastic Volatility-in-mean VARs with Time-Varying Skewness

摘要： 本文介绍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。 与之前的方法不同，所提出的模型允许波动率和偏度直接影响宏观经济变量。 我们提供了一个吉布斯抽样算法用于后验推断，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，通常超过波动率冲击的影响。 在伪实时预测实验中，所提出的模型在许多情况下优于现有的替代方法。 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了不确定性。 这些发现强调了纳入时变偏度对于捕捉宏观金融风险和提高预测性能的重要性。 显示英文摘要

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.