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交易心理学

Copula-GARCH模型下的两资产期权定价

Copula-GARCH versus dynamic conditional correlation: 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 (ES) of bivariate portfolios. We then try to answer two questions: First, Copula-GARCH模型下的两资产期权定价 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 Copula-GARCH模型下的两资产期权定价 statistics on bivariate data samples as independent variables to identify a parametric model yielding optimal portfolio VaR and ES 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 Copula-GARCH模型下的两资产期权定价 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 Copula-GARCH模型下的两资产期权定价 estimates is positively correlated with the portfolio marginals’ volatility, while the opposite is true for the elliptical copulas. For the Archimedean copulas in particular, the excess kurtosis of the marginals has a significant positive influence on quality of the VaR and ES estimates.

Copula-GARCH模型下的两资产期权定价

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Abstract

This paper minimizes the risk of Brent oil in a multivariate portfolio, with three risk-minimizing goals: variance, Copula-GARCH模型下的两资产期权定价 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 Copula-GARCH模型下的两资产期权定价 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 Copula-GARCH模型下的两资产期权定价 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 Copula-GARCH模型下的两资产期权定价 portfolio with these indexes has slightly lower modified VaR than a portfolio with Copula-GARCH模型下的两资产期权定价 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 Copula-GARCH模型下的两资产期权定价 downside risk of a portfolio. As Copula-GARCH模型下的两资产期权定价 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.

Copula-GARCH模型下的两资产期权定价

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Date and time: Fri, 19 Aug 2022 16:47:20 GMT

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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 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 dependence structure. There is seasonal seasonal Copula-GARCH模型下的两资产期权定价 effect; the summer holiday and Chinese Spring Festival have positive effects on the all destinations. Finally, most of the Copula-GARCH模型下的两资产期权定价 time, Thailand and Singapore have the highest conditional dependence. The result indicates that Thailand and Singapore have a complementary relationship.