Modeling the Dependence of Time Series Vector Using Copulas
Authors: 
Huang Ai Hua, Yi Wen DeAuthors Info & Claims
Huang Ai Hua, Yi Wen DeAuthors Info & ClaimsArticle No.: 14, Pages 1 - 4
Abstract
Copulas offer financial risk managers a powerful tool to analysis the dependence between risk factors. In this paper, we propose a new methodology based on the conditional probability of Markov chains of order 1 and copula theory to identify the dependence between time series of equity return. We combine these two theories to establish a model for the temporal dependence and the contemporaneous dependence of time series vector as well as an approach of parametric estimation of the model is proposed. A chi-square test and a parametric pseudo likelihood ratio statistic are employed as a guide to select a best fitting copula of data. Different models for the marginal distributions and several typical copulas have been fitted, compared and selected.
References
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Information & Contributors
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Published In
December 2022
199 pages
ISBN:9781450399548
DOI:10.1145/3588340
Copyright © 2022 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 November 2023
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- Research-article
- Research
- Refereed limited
Funding Sources
- Program for Innovation Team Building at Institutions of Higher Education in Chongqing (Grant No.KJTD201321)
Conference
ICBICC 2022
ICBICC 2022: 2022 International Conference on Big data, IoT, and Cloud Computing
December 2 - 4, 2022
Chengdu, China
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