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

An approach to modelling volatile financial return series using stationary d-vine cop-ula 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 (c) 2021 The Author(s). Published by Elsevier B.V. on behalf of EcoSta Econometrics and Statistics. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )