具有偏度选择的随机波动率模型
Stochastic volatility models with skewness selection
AI总结：
本文提出利用稀疏诱导先验在随机波动率模型中自动选择偏度参数为动态、静态或零，避免过度参数化，并通过债券收益率和货币因子两个实证应用验证了方法的有效性。
摘要：
本文通过允许时变偏度但不强制施加偏度，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，但它也带来了过度参数化的风险。我们提出的方法通过利用稀疏诱导先验，在数据驱动框架中自动选择偏度参数为动态、静态或零，从而缓解了这一担忧。我们考虑了两个实证应用。首先，在债券收益率应用中，动态偏度捕捉了货币宽松和紧缩的利率周期，这部分可由中央银行的职责来解释。在货币建模框架中，我们的模型表明，在考虑随机波动率后，套利因子中不存在偏度，这支持了套利崩溃是波动率飙升而非动态偏度所致这一观点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
