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

Martin Bladt and Alexander J. McNeil

Econometrics and Statistics, 2022, vol. 24, issue C, 27-48

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.11The analyses were carried out using R and the tscopula CRAN package, c.f (McNeil and Bladt, 2021), also available at https://github.com/ajmcneil/tscopula. In particular, it uses C++ code for vine copulas from the rvinecopulib package (Nagler and Vatter, 2020).

Keywords: Time series; Volatility models; Copulas; v-transforms; Vine copulas (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2452306221000800
Full text for ScienceDirect subscribers only. Contains open access articles

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:24:y:2022:i:c:p:27-48

DOI: 10.1016/j.ecosta.2021.07.004

Access Statistics for this article

Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi

More articles in Econometrics and Statistics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().