The stochastic volatility model is a popular tool for modeling the volatility of assets. The model is a nonlinear and non‐Gaussian state space model and presents some challenges not seen in general. Many approaches have been developed for Bayesian analysis that rely on numerically intensive techniques such as Markov chain Monte Carlo (MCMC). Convergence and mixing problems still plague MCMC algorithms used for the model. We present an approach that ameliorates the slow convergence and mixing problems when fitting stochastic volatility models. The approach accelerates the convergence by exploiting the geometry of one of the targets. We demonstrate the method on various numerical examples.

中文翻译：

---

关于随机波动率模型有效拟合的说明

随机波动率模型是一种流行的资产波动建模工具。该模型是一个非线性和非高斯状态空间模型，并提出了一些一般未见的挑战。已经开发了许多依赖于数值密集型技术的贝叶斯分析方法，例如马尔可夫链蒙特卡罗 (MCMC)。收敛和混合问题仍然困扰着用于模型的 MCMC 算法。我们提出了一种在拟合随机波动率模型时改善缓慢收敛和混合问题的方法。该方法通过利用目标之一的几何形状来加速收敛。我们在各种数值示例上演示了该方法。