Factor volatility spillover and its implications on factor premia

Shi, Huai-Long; Zhou, Wei‐Xing

doi:10.1016/j.intfin.2022.101631

Cited by 6 publications

(1 citation statement)

References 111 publications

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“…We further analyze the time‐varying spillover effect using a rolling‐window approach. Diebold and Yilmaz (2012) estimated the risk spillover using a 200‐day rolling window, whose size is widely followed (Naeem et al, 2022; Shi & Zhou, 2022; Xu et al, 2023). Baruník et al (2016) and Wu et al (2019) found that the results are robust with respect to window size.…”

Section: Methodsmentioning

confidence: 99%

Connectedness and risk spillover in China's commodity futures sectors

Long,

Yuan,

Jin

et al. 2024

Journal of Futures Markets

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This study employs minimum spanning tree and generalized forecast error variance decomposition methods to investigate the connectedness and risk spillovers across China's commodity sectors from January 2016 to December 2021. The results show that total connectedness within the commodity system is time varying. Chemical is the main risk driver, while other sectors occasionally dominate the system. These two methods achieve consistent results in identifying the systemically important sector and dynamic connectedness. In addition, we find that Chinese economic policy uncertainty and the investor sentiment index have significant impacts on total connectedness. Our findings have implications for preventing systemic risk for policymakers and managing commodity portfolio risk for investors.

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