Dynamic interrelationships among crude oil, green bond, and carbon markets: Evidence from fuzzy logic autoencoders
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This paper investigates the dynamic interrelationships among various markets covering crude oil, green bonds, and carbon emissions from January 2014 to October 2022, using a Fuzzy Logistic Autoencoder (FLAE) model, which elevates methodological sophistication and helps capturing intricate and complex relationships across the three markets. Different features of FLAE, such as identifying crossed lags and introducing a novel sigmoid-type activation function, enhance structural stability and establish the model as a reference for studying cross-temporal effects across markets. The key findings indicate that green bond returns negatively impact the returns of carbon emission allowances and Brent oil in the short and medium term. The impact of carbon emission allowance returns and oil returns on the forecast of green bond returns is comparatively trivial. Forecasting green bond returns is primarily driven by its short-term lags. These findings should be useful for portfolio managers in energy markets, environmentally conscious investors, and policy-makers concerned with financial sustainability amid the energy transition. © 2025 Elsevier B.V.
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Autoencoders, CO₂ emission allowances, Crude oil prices, Deep learning, Green bonds, Machine learning, Sustainable finance, Low emission, Auto encoders, Carbon emissions, Carbon markets, CO₂ emission allowance, Crude oil prices, Deep learning, Emission allowances, Green bond, Machine-learning, Sustainable finance, Crude oil price