An approach to modelling volatile financial return series using stationary d-vine copula processes combined with Lebesgue-measure-preserving transformations known as v-transforms is proposed. By developing a method of stochastically inverting v-transforms, models are constructed that can describe both stochastic volatility in the magnitude of price movements and serial correlation in their directions. In combination with parametric marginal distributions it is shown that these models can rival and sometimes outperform well-known models in the extended GARCH family.¹

中文翻译：

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使用 d-vines 和 v-transforms 的时间序列 copula 模型

提出了一种使用固定 d-vine copula 过程与称为 v-transforms 的 Lebesgue-measure-preserving 变换对波动性财务回报序列进行建模的方法。通过开发一种随机反转 v 变换的方法，构建的模型可以描述价格变动幅度的随机波动性和它们方向上的序列相关性。与参数边际分布相结合，表明这些模型可以与扩展 GARCH 家族中的知名模型相媲美，有时甚至优于知名模型。¹