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股指期货与现货之间的相依结构是Copula理论在金融分析中套期保值、组合风险对冲及价格发现等应用的热点。考虑到新息对价格的非对称冲击和相依结构的时变特征,利用GJR-GARCH模型对股指期货和现货的收益率序列建模,选用DCC方程刻画二者之间时变相关系数的演化结构,构建时变T-Copula-GJR-GARCH模型。针对沪深300指数现货与期货5～60分钟的高频数据,分尺度拟合时变T-Copula-GJR-GARCH(1,1)-t模型,结果表明相依结构随时间尺度变化而变化,这或许可由市场微观结构差异及投资者的异质性所解释,进而本文从多尺度的视角揭示了我国股指期货与现货之间的时变相依模式。
The structure of the interdependence between stock index futures and spot is a hot spot for Copula’s theory of hedging, portfolio hedging and price discovery in financial analysis. Considering the time-varying characteristics of the asymmetric impact and the dependent structure of the new interest on the price, the GJR-GARCH model is used to model the yield series of the stock index futures and the spot. The DCC equation is used to characterize the evolutionary structure of the time-varying correlation coefficient , A time-varying T-Copula-GJR-GARCH model is constructed. According to the T-Copula-GJR-GARCH (1,1) -t model, the time-varying F-Copula-GJR-GARCH (1,1) -t model is fitted to the high frequency data of spot and futures of Shanghai and Shenzhen 300 Index for 5-60 minutes. This may be explained by the differences in market microstructure and the heterogeneity of investors. Furthermore, this article reveals the time-varying dependency pattern between China’s stock index futures and spot from a multi-scale perspective.