Research Article
Stable Random Vector and Gaussian Copula for Stock Market Data
@INPROCEEDINGS{10.1007/978-3-030-92942-8_16, author={Truc Giang Vo Thi}, title={Stable Random Vector and Gaussian Copula for Stock Market Data}, proceedings={Nature of Computation and Communication. 7th EAI International Conference, ICTCC 2021, Virtual Event, October 28--29, 2021, Proceedings}, proceedings_a={ICTCC}, year={2022}, month={1}, keywords={Stable distribution Gaussian copula Stock market}, doi={10.1007/978-3-030-92942-8_16} }- Truc Giang Vo Thi
Year: 2022
Stable Random Vector and Gaussian Copula for Stock Market Data
ICTCC
Springer
DOI: 10.1007/978-3-030-92942-8_16
Abstract
A lot of real-world data sets have heavy tailed distribution, while the calculations for these distributions in multidimensional case are complex. The paper shows a method to investigate data of multivariate heavy-tailed distributions. We show that for any given number(\alpha \in (0; 2],)each Gaussian copula is also the copula of an(\alpha )-stable random vector. Simultaneously, every random vector is(\alpha )-stable if its marginals are(\alpha )-stable and its copula is a Gaussian copula. The result is used to build up a formula representing density functions of(\alpha )-stable random vectors with Gaussian copula. Adopting a new tool, the paper points out that in most of cases, vectors of daily returns in stock market data have multivariate stable distributions with Gaussian copulas and we propose a new method to choose an investment portfolio by computing the distribution of linear combination of components of stable random vector. Dataset of 4 stocks on HOSE was analyzed.
