Using high frequency data and connectedness measures based on a time-varying parameter vector autoregression (TVP-VAR) model, we study dynamic connectedness among the realized volatility of 15 commodity futures (Gold, Heating oil, Light crude oil, Natural gas, Copper, Platinum, Cocoa, Coffee, Corn, Cotton, Orange Juice, Soybean, Soybean meal, Sugar, and Wheat) from September 22, 2008 to May 28, 2020. The results show strong and moderate levels of volatility connectedness among energy and metals and moderate connectedness levels within the group of agricultural commodities. Cross-commodity connectedness can explain a large portion of volatility connectedness in some cases, highlighting the importance of conducting realized volatility connectedness within a model allowing realized volatilities to be endogenously and simultaneously determined. Connectedness is robust to alternative specifications and varies with time. It is mostly driven by macroeconomic variables and uncertainty, including the term spread of interest rates and real economic activity. However, the analysis shows that some of the drivers of connectedness differ between upper and lower quantiles.

使用基于时变参数矢量自回归（TVP-VAR）模型的高频数据和连通性度量，我们研究了15种商品期货（黄金，取暖油，轻质原油，天然气，铜，从2008年9月22日至2020年5月28日的铂，可可，咖啡，玉米，棉花，橙汁，大豆，大豆粉，糖和小麦）。结果显示，能源和金属之间的波动性关联度处于中等水平，且处于中等水平农产品组内的联系程度。跨商品联系可以在某些情况下解释大部分的波动联系，突出了在模型中进行已实现的波动联系的重要性，该模型允许内生并同时确定已实现的波动。连接性对替代规范具有鲁棒性，并且会随时间而变化。它主要由宏观经济变量和不确定性驱动，包括利率和实际经济活动的期限利差。但是，分析表明，上分位数和下分位数之间的一些连通性驱动因素有所不同。