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

Martin Bladt; Alexander John McNeil

doi:10.1016/j.ecosta.2021.07.004

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

Martin Bladt, Alexander John McNeil

The York Management School

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	27-48
Number of pages	22
Journal	Econometrics and Statistics
Volume	24
Early online date	24 Sept 2022
DOIs	https://doi.org/10.1016/j.ecosta.2021.07.004
Publication status	Published - 1 Oct 2022

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10.1016/j.ecosta.2021.07.004Licence: CC BY

Time series copula models using d-vines and v-transforms
© 2021 The Author(s).
Final published version, 3.07 MBLicence: CC BY

Cite this

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