Time series copula models using d-vines and v-transforms
| Autor: | Martin Bladt, Alexander J. McNeil |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Economics and Econometrics Statistical Finance (q-fin.ST) Stochastic volatility Series (mathematics) Autoregressive conditional heteroskedasticity 05 social sciences Copula (linguistics) Autocorrelation Quantitative Finance - Statistical Finance Magnitude (mathematics) 01 natural sciences Methodology (stat.ME) FOS: Economics and business 010104 statistics & probability 0502 economics and business Econometrics 0101 mathematics Statistics Probability and Uncertainty Marginal distribution Statistics - Methodology 050205 econometrics Parametric statistics Mathematics |
| Zdroj: | Econometrics and Statistics. 24:27-48 |
| ISSN: | 2452-3062 |
| DOI: | 10.1016/j.ecosta.2021.07.004 |
| Popis: | 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. 1 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect