Dependence properties of stochastic volatility models
该文证明大多数随机波动率模型满足物理相依性和可逼近性条件，即使其取值在抽象希尔伯特空间中，从而为这些模型应用基于伯努利移位建立的推断方法提供了基础。
The concepts of physical dependence and approximability have been extensively used over the past two decades to quantify nonlinear dependence in time series. We show that most stochastic volatility models satisfy both dependence conditions, even if their realizations take values in abstract Hilbert spaces, thus covering univariate, multi‐variate and functional models. Our results can be used to apply to general stochastic volatility models a multitude of inferential procedures established for Bernoulli shifts.