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

Martin Bladt, Alexander John McNeil

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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.
Original languageEnglish
Article number27-48
Number of pages22
JournalEconometrics and Statistics
Early online date24 Sept 2022
Publication statusPublished - 1 Oct 2022

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