Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/340652 
Year of Publication: 
2025
Citation: 
[Journal:] Borsa İstanbul Review [ISSN:] 2214-8469 [Volume:] 25 [Issue:] 6 [Year:] 2025 [Pages:] 1518-1529
Publisher: 
Elsevier, Amsterdam
Abstract: 
This paper uses quantile-on-quantile kernel-regularised least squares (QQKRLS) and quantile-on-quantile Granger causality (QQGC) methods to examine how the S&P Global Large MidCap Biodiversity Index (GBI) heterogeneously influences the stock markets of the G7 countries and China. Our empirical analysis of data from 2019 to 2025 demonstrates that the risk of biodiversity loss has a significant, quantile-dependent, and non-linear effect on these national stock markets. The findings reveal that the stock markets of developed European economies display highly sensitive positive responses to GBI fluctuations, whilst the United States' stock market exhibits complex cross-causality structures. Conversely, the Chinese stock market shows pronounced asymmetric and stage-specific characteristics, demonstrating significant vulnerability during periods of heightened risk. Robustness tests using quantile regression and ordinary least squares (OLS) regression further validate the reliability of these principal findings. This research provides substantial empirical evidence on the cross-national transmission mechanisms of sustainable finance. It holds significant theoretical value and offers practical implications for investors developing quantile-sensitive investment strategies and for policymakers refining green finance regulatory frameworks. The results forecast that biodiversity-related financial risks will continue to have heterogeneous effects across different market conditions and geographical regions.
Subjects: 
Biodiversity risk
International stock markets
Quantile-on-quantile Granger causality
Quantile-on-quantile kernel regularised least squares
Sustainable finance
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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