Idiosyncratic tail risk spillover of Chinese commodity futures markets

This study proposes a novel generalized dynamic factor tail-restricted integrated regression function (GDF-IRF) network to investigate the idiosyncratic tail risk spillover in Chinese commodity futures markets. The core advance of this model lies in its design as a directed tail-conditional dependence network, which is built on factor-filtered idiosyncratic components and aggregated over short-, medium-, and long-term horizons. Using data from 1,604 trading days spanning March 2018 to November 2024, we analyze idiosyncratic tail risk transmission patterns across different frequency domains and crisis periods (e.g., the US-China trade friction, the COVID-19 pandemic, and the Russia-Ukraine war). Our results show that: (1) The contagion in the idiosyncratic tail risk network is significantly higher than that in the tail risk network. Furthermore, the former is stronger in the upper tail than in the lower tail, whereas the latter follows the opposite trend. (2) Coal-related and soybean futures are the main idiosyncratic risk transmitters in the lower and upper tails, while oil-related and soybean meal futures act as idiosyncratic risk receivers in the lower and upper tails. (3) The regression results indicate that both commodity characteristics and macroeconomic factors drive futures’ contagiousness, but their effects are asymmetric. Investors and policymakers could use our findings as early warning tools to identify influential risk spreaders during crisis periods.