Risk–Return Dynamics and Volatility Connectedness of ESG-Aligned Assets: Extant Evidence, Patterns, and Inquiries

Laxman Tandan, Rudra P. Pradhan and Bikash Rana

Complexity, 2026, vol. 2026, 1-17

Abstract: The advancement of ESG-aligned assets has increased attention in comprehending their risk–return dynamics and volatility behavior in the financial market; however, existing research provides fragmented and mixed evidence, suggesting the necessity of an integrated synthesis. This study reviews the prior knowledge to consolidate and critically evaluate the risk–return dynamics and volatility characteristics of ESG-aligned assets. Using a structured SPAR-4 SLR approach, 78 peer-reviewed published papers from 2015 to 2025 were retrieved from Scopus and Web of Science. The review employs descriptive bibliometric techniques and thematic assessment to synthesize the current literature. The findings identify four major interdependent themes: volatility connectedness, environment/climate risks, diversification strategies, and ESG sentiment. Overall, ESG-aligned assets often provide diversification and resilience benefits, but the direction and magnitude of these benefits rely on asset class, market development, regulatory setting, information quality, and crisis regime. The study synthesizes fragmented evidence and identifies research gaps while offering theoretical, practical, and policy insights for ESG asset pricing and sustainable finance research.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/complexity/2026/3038268.pdf (application/pdf)
http://downloads.hindawi.com/journals/complexity/2026/3038268.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:3038268

DOI: 10.1155/cplx/3038268

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().