Modeling the joint distribution of spot and futures returns is crucial for establishing optimal hedging strategies. This paper proposes a new class of dynamic copula-GARCH models that exploits information from high-frequency data for hedge ratio estimation. The copula theory facilitates constructing a flexible distribution; the inclusion of realized volatility measures constructed from high-frequency data enables copula forecasts to swiftly adapt to changing markets. By using data concerning equity index returns, the estimation results show that the inclusion of realized measures of volatility and correlation greatly enhances the explanatory power in the modeling. Moreover, the out-of-sample forecasting results show that the hedged portfolios constructed from the proposed model are superior to those constructed from the prevailing models in reducing the (estimated) conditional hedged portfolio variance. Finally, the economic gains from exploiting high-frequency data for estimating the hedge ratios are examined. It is found that hedgers obtain additional benefits by including high-frequency data in their hedging decisions; more risk-averse hedgers generate greater benefits.

中文翻译：

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期货的动态套期：具有高频数据的基于copula的GARCH模型

对现货和期货收益的联合分配进行建模对于建立最佳对冲策略至关重要。本文提出了一类新的动态copula-GARCH模型，该模型利用高频数据中的信息进行套期保值比率估计。copula理论有助于构建灵活的分布；根据高频数据构建的已实现的波动率度量，可以使copula预测迅速适应不断变化的市场。通过使用有关股指回报的数据，估计结果表明，将已实现的波动率和相关性测度包括在内，可以大大增强建模的解释力。此外，样本外预测结果表明，在减少（估计）有条件套期保值投资组合方差方面，从建议的模型构建的套期保值投资组合优于从现有模型构建的套期保值投资组合。最后，研究了利用高频数据估算套期比率的经济收益。人们发现，套期保值者通过将高频数据纳入对冲决策中而获得了额外的收益。更多规避风险的套期保值者会产生更大的收益。