Forecasting corporate bond returns amid climate change risk: A dynamic forecast combination approach

Feng Ma, Yangli Guo, Qin Luo and Juandan Zhong

Journal of International Money and Finance, 2025, vol. 154, issue C

Abstract: This study examines the predictability of Chinese corporate bond returns in the context of climate change risk using the Climate Change Concern Index (CCCI) derived from text data. The results show that the CCCI has strong predictive power in both the in-sample and out-of-sample analyses, especially for AAA-rated bonds and bonds with shorter maturities. In addition, applying the Dynamic Forecast Combination method shows that state factors such as economic activity significantly improve the overall predictive power of bond returns, especially in times of low economic activity. The inclusion of climate risk in the prediction of bond returns also brings tangible economic benefits, as shown by the increased certainty-equivalent returns and Sharpe ratios. These results show that climate risk is a significant source of systemic risk and can predict risk premiums in the bond market. Investors should also consider climate risk and economic conditions when constructing portfolios.

Keywords: Corporate Bonds Excess Returns; Dynamic Forecast Combination; Out-of-sample Forecasts (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jimfin:v:154:y:2025:i:c:s0261560625000592

DOI: 10.1016/j.jimonfin.2025.103324

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