Time series copula models using d-vines and v-transforms

TL;DR

This paper introduces a novel approach to modeling financial return series using stationary d-vine copula processes combined with v-transforms, capturing volatility and serial correlation effectively.

Contribution

It develops a method for stochastically inverting v-transforms within copula models, enhancing the ability to model stochastic volatility and serial dependence.

Findings

Models can describe stochastic volatility and serial correlation.

Models outperform some extended GARCH models.

Effective combination with parametric marginals.

Abstract

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.