标题： 一个具有相依误差的半参数随机波动率模型 显示英文标题

标题： A Semiparametric Stochastic Volatility Model with Dependent Errors

摘要： 本文提出了一种半参数随机波动率（SV）模型，该模型放松了回归和波动误差项中严格的高斯假设，允许它们遵循具有潜在依赖性的灵活非参数分布。通过将此框架整合到贝叶斯马尔可夫链蒙特卡洛（MCMC）方法中，该模型能够有效捕捉金融收益率数据中经常观察到的厚尾、偏度和其他复杂特征。 在相关高斯和t分布误差设置下的模拟研究表明，与传统的基于高斯的方法以及流行的贝叶斯实现相比，所提出的方法在估计模型参数和波动性时表现出更低的偏差和方差。我们对真实世界金融数据进行了实证应用，进一步突显了该模型的实际优势：它提供的波动率估计能更准确地响应大幅波动，反映现实市场的行为。 这些发现表明，引入的半参数SV框架为金融计量经济学提供了更稳健和适应性强的工具，特别是在非高斯且存在依赖性的收益动态场景中具有重要意义。 显示英文摘要

摘要： This paper proposes a semiparametric stochastic volatility (SV) model that relaxes the restrictive Gaussian assumption in both the return and volatility error terms, allowing them to follow flexible, nonparametric distributions with potential dependence. By integrating this framework into a Bayesian Markov Chain Monte Carlo (MCMC) approach, the model effectively captures the heavy tails, skewness, and other complex features often observed in financial return data. Simulation studies under correlated Gaussian and Student's t error settings demonstrate that the proposed method achieves lower bias and variance when estimating model parameters and volatility compared to traditional Gaussian-based and popular Bayesian implementations. We conduct an empirical application to the real world financial data, which further underscores the model's practical advantages: it provides volatility estimates that respond more accurately to large fluctuations, reflecting real-world market behavior. These findings suggest that the introduced semiparametric SV framework offers a more robust and adaptable tool for financial econometrics, particularly in scenarios characterized by non-Gaussian and dependent return dynamics.