文章基本信息

标题：Modeling Conditional Dependence of Stock Returns Using a Copula-based GARCH Model
本地全文：下载
作者：Eun-Joo Lee ; Noah Klumpe ; Jonathan Vlk 等
期刊名称：International Journal of Statistics and Probability
印刷版ISSN：1927-7032
电子版ISSN：1927-7040
出版年度：2017
卷号：6
期号：2
页码：32
DOI：10.5539/ijsp.v6n2p32
出版社：Canadian Center of Science and Education
摘要：Investigating dependence structures of stocks that are related to one another should be an important consideration in managing a stock portfolio, among other investment strategies. To capture various dependence features, we employ copula to overcome the limitations of traditional linear correlations. Financial time series data is typically characterized by volatility clustering of returns that influences an estimate of a stock’s future price. To deal with the volatility and dependence of stock returns, this paper provides procedures of combining a copula with a GARCH model which leads to the construction of a multivariate distribution. Using the copula-based GARCH approach that describes the tail dependences of stock returns, we carry out Monte Carlo simulations to predict a company’s movements in the stock market. The procedures are illustrated in two technology stocks, Apple and Samsung.