Financing Green Solutions: Asset Returns and Tail Risks

Authors

Eugenijus Gabrielius Ivanauskas
Vilnius University, Institute of Applied Mathematics
Liepa Urbonaitė
Vilnius University, Institute of Applied Mathematics
Saulius Jokubaitis
Vilnius University, Institute of Applied Mathematics
DOI: https://doi.org/10.18690/um.fov.5.2026.4

Synopsis

Financing Green Solutions: Asset Returns and Tail Risks: The translation of green technologies from theoretical potential to industrial adoption hinges on the stability of the financial mechanisms that fund them. This Chapter investigates the risk dynamics and dependence structures of the Green financial ecosystem, comprising Green Bonds, Clean Energy ETFs, and Carbon Credits. Employing a rolling-window ARMA-GARCH-Vine Copula framework we map the evolving topology of sustainable finance and test the "decoupling hypothesis". The analysis reveals that while Green Bonds have successfully decoupled and act as effective portfolio diversifiers, Clean Energy equities remain deeply integrated with broad market risks, functioning as a centralized "hub" for volatility transmission. We further identify a distinct structural shift around mid-2024, where with the fade of the post-pandemic volatility the market network stabilizes into an R-Vine structure that transmits shocks more efficiently. Finally, we assess the viability of clean cryptocurrencies, finding them structurally incompatible with institutional hedging strategies due to extreme tail-risk dependence.

Author Biographies

Eugenijus Gabrielius Ivanauskas, Vilnius University, Institute of Applied Mathematics

Eugenijus Gabrielius Ivanauskas is a first-year Master’s student in Mathematics at the same faculty. In 2025, he completed his Bachelor’s degree in Econometrics at Vilnius University, graduating cum laude. His main interests include mathematical modelling, statistics, and financial risk, which he plans to further develop through PhD studies af-ter 2027. Alongside the studies, he works at Danske Bank in Model Risk Management, where he validates quantitative models related to financial risk, including ISDA SIMM, Value-at-Risk models for asset management, and CLO pricing frameworks. His expe-rience combines a solid theoretical background with practical applications in finance.

Vilnius, Lithuania. E-mail: gabrielius.ivanauskas@mif.stud.vu.lt

Liepa Urbonaitė, Vilnius University, Institute of Applied Mathematics

Liepa Urbonaitė is a second-year Bachelor’s student in Data Science at Vilnius Uni-versity. Her primary academic interests lie in mathematics and statistics, which form the core of her studies and future aspirations. She plans to further deepen her knowl-edge in these areas through Master’s studies. Alongside her academic work, she is em-ployed as a tutor, teaching chemistry, mathematics, and physics. This role allows her to strengthen her analytical thinking while helping others build a solid foundation in scien-tific subjects. Her experience reflects a strong combination of theoretical understanding and practical application in problem-solving. She is also a recipient of multiple na-tional awards in chemistry and has represented Lithuania in international competitions, earning bronze and silver medals at the International Junior Science Olympiad and the European Olympiad of Experimental Sciences.

Vilnius, Lithuania. E-mail: liepa.urbonaite@mif.stud.vu.lt

Saulius Jokubaitis, Vilnius University, Institute of Applied Mathematics

Saulius Jokubaitis is an assistant professor at Vilnius University, Institute of Applied Mathematics, Faculty of Mathematics and Informatics. He holds a PhD in Mathematics from Vilnius University. His research focus is machine learning, econometrics, time-series analysis and risk modelling. In addition to his academic work, he serves as a consultant in the financial sector, applying advanced risk modelling techniques to real-world business challenges.

Vilnius, Lithuania. E-mail: saulius.jokubaitis@mif.vu.lt

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Volume

Artificial Intelligence and Environmental Challenges: Research Insights and Emerging Solutions

Pages

57-86

Published

June 18, 2026

Categories

Copyright (c) 2026 University of Maribor, University of Maribor Press

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Ivanauskas, E. G., Urbonaitė, L., & Jokubaitis, S. (2026). Financing Green Solutions: Asset Returns and Tail Risks. In R. Leskovar (Ed.), Artificial Intelligence and Environmental Challenges: Research Insights and Emerging Solutions (pp. 57-86). University of Maribor Press. https://doi.org/10.18690/um.fov.5.2026.4