We consider a mixed moving average (MMA) process X driven by a L\'evy basis and prove that it is weakly dependent with rates computable in terms of the moving average kernel and the characteristic quadruple of the L\'evy basis. Using this property, we show conditions ensuring that sample mean and autocovariances of X have a limiting normal distribution. We extend these results to stochastic volatility models and then investigate a Generalized Method of Moments estimator for the supOU process and the supOU stochastic volatility model after choosing a suitable distribution for the mean reversion parameter. For these estimators, we analyze the asymptotic behavior in detail.

翻译：我们考虑的是由L\'evy基调驱动的混合移动平均(MMA) X进程,并证明它与按移动平均内核和L\'evy基点的四倍特性计算的计算率相比依赖性不强。使用此属性,我们展示了确保X的样本平均值和自动变异的正常分布有限的条件。我们将这些结果推广到随机多变性模型,然后调查SupOU进程和SupOU的超常随机多变性模型的通用模型估计方法,然后选择了中值再转换参数的适当分布。我们对这些估计者进行详细的非现行为分析。