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The copula directional dependence by stochastic volatility models

Authors
Kim, Jong-MinHwang, S. Y.
Issue Date
Apr-2019
Publisher
TAYLOR & FRANCIS INC
Keywords
Beta regression model; Copula; Directional dependence; Stochastic volatility model
Citation
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.48, no.4, pp.1153 - 1175
Journal Title
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume
48
Number
4
Start Page
1153
End Page
1175
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/3700
DOI
10.1080/03610918.2017.1406512
ISSN
0361-0918
Abstract
This paper proposes a copula directional dependence by using a bivariate Gaussian copula beta regression with Stochastic Volatility (SV) models for marginal distributions. With the asymmetric copula generated by the composition of two Plackett copulas, we show that our SV copula directional dependence by the Gaussian copula beta regression model is superior to the Kim and Hwang (2016) copula directional dependence by an asymmetric GARCH model in terms of the percent relative efficiency of bias and mean squared error. To validate our proposed method with the real data, we use Brent Crude Daily Price (BRENT), West Texas Intermediate Daily Price (WTI), the Standard & Poor's 500 (SP) and US 10-Year Treasury Constant Maturity Rate (TCM) so that our copula SV directional dependence is overall superior to the Kim and Hwang (2016) copula directional dependence by an asymmetric GARCH model in terms of precision by the percent relative efficiency of mean squared error. In terms of forecasting using the real financial data, we also show that the Bayesian SV model of the uniform transformed data by a copula conditional distribution yields an improvement on the volatility models such as GARCH and SV.
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