CD-vine model for capturing complex dependence

Date

2020-10-26

Author

Ozan Evkaya, O.
Yozgatlıgil, Ceylan
Sevtap Selcuk-Kestel, A.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Copula based finite mixture models allow us to capture the dependence between random variables more flexibly. Although bivariate case of finite mixture models has been commonly studied, limited efforts have been spent on finite mixture of vines. Instead of using classical mixture models, it is possible to incorporate C-vines into the D-vine model (CD-vine) to understand both the dependence among the variables over different time points. The aim of this study is to create a CD-vine mixture model expressing the dependencies between variables in temporal order. To achieve this, cumulative distribution function values generated within the time components are tied together with D-vine probabilistically. With this approach, dependence structure between variables at each time point is explained by C-vine and the dependence among the time points is captured by the D-vine model. The performance of the proposed CD-vine model is validated using simulated data and applied on four stock market indices.

Subject Keywords

Statistics, Probability and Uncertainty, Statistics and Probability, Mixture model, C-vine, D-vine, CD-vine mixture, stock market indices

URI

https://hdl.handle.net/11511/90043

Journal

Journal of Applied Statistics

DOI

https://doi.org/10.1080/02664763.2020.1834519

Collections

Department of Statistics, Article

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Citation Formats

O. Ozan Evkaya, C. Yozgatlıgil, and A. Sevtap Selcuk-Kestel, “CD-vine model for capturing complex dependence,” Journal of Applied Statistics, pp. 1–15, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/90043.