Decoding energy market turbulence: A TVP-VAR connectedness analysis of climate policy uncertainty and geopolitical risk shocks

Ling Liu, Mohamad H. Shahrour, Michal Wojewodzki and Alireza Rohani

Technological Forecasting and Social Change, 2025, vol. 210, issue C

Abstract: The ongoing escalation in geopolitical and climate uncertainties, coupled with the urgent issue of climate change, has profoundly affected the economic and political landscape, significantly increasing volatility in the energy and financial markets. This study investigates the dynamic interactions and spillover effects between geopolitical risks (GPR) and U.S. climate policy uncertainty (CPU) indices, energy markets (crude oil and natural gas prices), and U.S. 10-year Treasury yields from January 2008 to December 2023. We use the time-varying parameter vector autoregression (TVP-VAR) model to capture the studied nexus's nonlinear and evolving nature. Findings show that GPR and CPU jointly affect the volatility and connectedness of the studied markets. While GPR has immediate and more pronounced effects, particularly on oil prices, CPU exerts a more prolonged and diffuse impact. Furthermore, the results indicate that oil prices (U.S. Treasury yields) are the shocks' primary transmitter (receiver) to (from) other markets. The study suggests that policymakers should consider diversifying energy sources and enhancing strategic reserves to mitigate the adverse effects of these uncertainties. Additionally, the findings support an expedited transition to renewable energy sources, less sensitive to geopolitical and policy-related disruptions, in alignment with global efforts to combat climate change.

Keywords: Geopolitical risk; Climate policy uncertainty; Time-varying parameter vector autoregression; Energy markets; U.S. Treasury yields (search for similar items in EconPapers)
JEL-codes: C32 G15 Q41 Q48 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:210:y:2025:i:c:s0040162524006619

DOI: 10.1016/j.techfore.2024.123863

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