This paper studies the parameter estimation for Ornstein–Uhlenbeck stochastic volatility models driven by Lévy processes. We propose computationally efficient estimators based on the method of moments that are robust to model misspecification. We develop an analytical framework that enables closed-form representation of model parameters in terms of the moments and autocorrelations of observed underlying processes. Under moderate assumptions, which are typically much weaker than those for likelihood methods, we prove large-sample behaviors for our proposed estimators, including strong consistency and asymptotic normality. Our estimators obtain the canonical square-root convergence rate and are shown through numerical experiments to outperform likelihood-based methods.

中文翻译：

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LÉVY 驱动的 Ornstein-UHLENBECK 随机波动率模型的矩估计方法

本文研究了 Lévy 过程驱动的 Ornstein-Uhlenbeck 随机波动率模型的参数估计。我们提出了基于对模型错误指定具有鲁棒性的矩量方法的计算高效估计器。我们开发了一个分析框架，可以根据观察到的基本过程的矩和自相关来实现模型参数的封闭形式表示。在通常比似然方法弱得多的中等假设下，我们证明了我们提出的估计量的大样本行为，包括强一致性和渐近正态性。我们的估计器获得了典型的平方根收敛速度，并通过数值实验证明其优于基于似然的方法。