US-China Tensions and renewable energy markets: A Dynamic Bayesian Inference Approach

Naysary, Babak and Ghafoor, Abdul (2026) US-China Tensions and renewable energy markets: A Dynamic Bayesian Inference Approach. Working Paper 44. Centre for Accountancy Finance and Economics (CAFE), Birmingham City Business School, Birmingham City University. (Submitted)

Preview

Text WP_44.pdf - Submitted Version Download (789kB)

Abstract

This paper examines the impact of US-China trade tensions on renewable energy markets from 2010-2024. Using a Dynamic Bayesian Inference approach, we quantify time-varying sensitivities of four NASDAQ renewable energy indices: Solar, Wind, Geothermal, and Green Economy. Our findings reveal significant heterogeneity in market responses. These results suggest that certain renewable sectors have developed effective resilience mechanisms to geopolitical risks.

Item Type:	Monograph (Working Paper)
Identification Number:	44
Dates:	Date Event 30 September 2026 Submitted
Uncontrolled Keywords:	Dynamic Bayesian Inference, US-China trade tensions, renewable energy
Subjects:	CAH15 - social sciences > CAH15-02 - economics > CAH15-02-01 - economics
Divisions:	Business School > Accountancy, Finance and Economics Business School > Accountancy, Finance and Economics > Centre for Accountancy Finance and Economics
Depositing User:	Gemma Tonks
Date Deposited:	22 Oct 2026 16:20
Last Modified:	22 Oct 2026 16:20
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17279

Actions (login required)

View Item