Copula-GARCH versus dynamic conditional correlation: Copula-GARCH模型下的两资产期权定价 Copula-GARCH模型下的两资产期权定价 Copula-GARCH模型下的两资产期权定价 an empirical study on VaR and ES forecasting accuracy
In this paper, we analyze the accuracy of the copula-GARCH and Dynamic Conditional Correlation (DCC) models for forecasting the value-at-risk (VaR) and expected shortfall (Copula-GARCH模型下的两资产期权定价 ES) of bivariate portfolios. We then try to answer two questions: First, does the correlation-based DCC model outperform the copula models? Second, how can the optimal model for forecasting portfolio risk be identified via in-sample analysis? We address these questions using an extensive empirical study of 1,500 bivariate portfolios containing data on stocks, commodities and foreign exchange futures. Furthermore, we propose to use linear discriminant analysis estimated from descriptive statistics on bivariate data samples as independent variables to identify a parametric model yielding optimal portfolio VaR and ES Copula-GARCH模型下的两资产期权定价 estimates. In particular, we try to answer the question whether the quality of a parametric model’s VaR and ES estimates is driven by common data characteristics. The results show that the proposed use of linear discriminant analysis is superior to both the Kullback-Leibler Information Criterion and several copula goodness-of-fit tests in terms of overall classification accuracy. Furthermore, the results show that the quality of the DCC model’s VaR and ES estimates is positively correlated with the portfolio marginals’ volatility, while the opposite is true for the elliptical copulas. For Copula-GARCH模型下的两资产期权定价 the Archimedean copulas in particular, the excess kurtosis of the marginals has a significant positive influence on quality of the VaR and ES estimates.
Please Copula-GARCH模型下的两资产期权定价 note that Internet Explorer version 8.x is not supported as of January 1, 2016. Please refer to this support page for more information.
Borsa Istanbul Review
Add to Mendeley
This paper minimizes the risk of Brent oil in a multivariate portfolio, with three risk-minimizing goals: variance, parametric value-at-risk (VaR), and semiparametric value-at-risk. Brent oil is combined with five emerging ASEAN (Association of Southeast Asian Nations) stock indexes and five more developed non-ASEAN indexes. The preliminary dynamic equiciorrelation estimates indicate that the ASEAN stock indexes are less integrated and thus potentially better for diversification purposes. The portfolio results show that the ASEAN indexes are better hedges for oil in terms of minimum variance and minimum VaR. However, although the ASEAN indexes have higher extreme risk, we find that a portfolio with these indexes has slightly lower modified VaR than a portfolio with the non-ASEAN indexes. The reason is probably the higher variance and higher equicorrelation of the non-ASEAN indexes, because these inputs affect the value of the modified downside risk of a portfolio. As a complementary analysis, we put a 50 percent constraint on Brent in the portfolios, and then the portfolios with the non-ASEAN indexes have better risk-minimizing results.
Our systems have detected unusual traffic activity from your network. Please complete this reCAPTCHA to demonstrate that it's you making the requests and not a robot. If you are having trouble seeing or completing this Copula-GARCH模型下的两资产期权定价 challenge, this page may help. If you continue to experience issues, you can contact JSTOR support.
Block Reference: #9750b4dc-1fde-11ed-80e9-697064576f42
IP: 188.18.253.Copula-GARCH模型下的两资产期权定价 215
Date and time: Fri, 19 Aug 2022 16:47:20 GMT
©2000- ITHAKA. All Rights Reserved. JSTOR®, the JSTOR logo, JPASS®, and ITHAKA® are registered trademarks of ITHAKA.
Dynamic Copula-Based GARCH Model Analysis China Outbound Tourism Demand
This Copula-GARCH模型下的两资产期权定价 paper used dynamic copula-GARCH model to analysis volatility and dependency of China outbound tourism to four leading countries, namely, Thailand, Singapore, South Korea, and Japan. It Copula-GARCH模型下的两资产期权定价 was found that Japan, South Korea, and Thailand have high volatilities. Furthermore, the conditional dependence is time-varying and different copulas generate different the time path Copula-GARCH模型下的两资产期权定价 dependence structure. There is seasonal seasonal effect; the summer holiday and Chinese Spring Festival have positive effects on the all destinations. Finally, most of the time, Thailand and Singapore have the highest conditional dependence. The result indicates that Thailand and Singapore have a complementary relationship.