AI-Carbon-Energy: Spillover effects and drivers in interconnected markets

Area of Science:

• Economics
• Environmental Science
• Technology

Background:

• Financial markets are increasingly interconnected.
• Understanding risk transmission between emerging markets like AI and established ones like energy and carbon is crucial.

Purpose of the Study:

• To investigate the spillover effects among AI, carbon, and energy markets.
• To identify macro and micro factors influencing these spillover dynamics.
• To analyze time-varying risk transmission patterns.

Main Methods:

• Time-varying parameter vector autoregression (TVP-VAR) model.
• Multivariate quantile-to-quantile regression (MM-QQ) model.

Main Results:

• Significant spillover effects were detected across all three markets.
• AI and new energy markets are key risk transmitters for returns and volatility.
• Traditional energy markets (except gas) transmit risk for skewness and kurtosis.
• The carbon market is consistently a net risk recipient.
• Spillover effects exhibit time-varying dynamics, with short-term effects on returns/skewness and long-term effects on volatility/kurtosis.

Conclusions:

• The interconnectedness of AI, carbon, and energy markets presents complex risk dynamics.
• Geopolitical risk, economic policy uncertainty, climate risk, AI progress, and investor attention influence these spillovers.
• Dynamic analysis is essential for understanding market interdependencies.