标题： 具有偏度选择的随机波动率模型 显示英文标题

标题： Stochastic volatility models with skewness selection

摘要： 本文通过允许时间变化的偏度而不强制实施它，扩展了传统的随机波动率模型。 虽然动态不对称性可能捕捉未来资产收益的可能方向，但会带来过度参数化的风险。 我们提出的方法通过利用诱导稀疏性的先验来缓解这一担忧，在数据驱动的框架中自动选择偏度参数为动态、静态或零。 我们考虑了两个实证应用。 首先，在债券收益率应用中，动态偏度部分由央行的使命解释，捕捉了货币政策宽松和紧缩的利率周期。 在货币建模框架中，我们的模型在考虑随机波动率后表明动量因子没有偏度，这支持了动量崩溃是由于波动率激增而非动态偏度的结果这一观点。 显示英文摘要

摘要： This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically selects the skewness parameter as being dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening being partially explained by central banks' mandates. In an currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility which supports the idea of carry crashes being the result of volatility surges instead of dynamic skewness.