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Asset Returns and CO2 Emissions: Evidence on Contemporaneous and Lagged Connectedness

Author

Listed:
  • Fekria Belhouichet
  • Guglielmo Maria Caporale
  • Luis Alberiko Gil-Alana

Abstract

This study examines the contemporaneous and lagged connectedness between the daily returns of AI and robotics-related assets, a global stock market index, commodity prices (gold and Brent crude oil), cryptocurrencies, and a carbon index over the period from 3 January 2023 to 30 September 2025, against a backdrop of persistent geopolitical tensions, using the innovative R² connectedness method developed by Balli et al. (2023). The results reveal that contemporaneous effects predominate over lagged ones. Furthermore, AI and robotics-related assets behave primarily as net emitters of shocks, as does the MSCI World Index, which exerts positive contagion effects and plays a central role in risk transmission. By constrast, gold and Brent crude oil act as net receivers of shocks, which in the case of the former reflects its role as a safe-haven asset. Cryptocurrencies instead exhibit heterogeneous dynamics : Cardano (ADA) acts as a net transmitter of shocks, while Bitcoin (BTC) and Stellar (XLM) behave more as receivers, contributing to market stability. Finally, the CO₂ index displays net negative connectedness, which confirm its role as a receiver of shocks. These findings provide useful information to investors and portfolio managers for risk diversification purposes and to policy-makers for ensuring financial stabilily, especially during periods of market turbulence.

Suggested Citation

  • Fekria Belhouichet & Guglielmo Maria Caporale & Luis Alberiko Gil-Alana, 2025. "Asset Returns and CO2 Emissions: Evidence on Contemporaneous and Lagged Connectedness," CESifo Working Paper Series 12333, CESifo.
    • Handle: RePEc:ces:ceswps:_12333
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    References listed on IDEAS

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    1. Mensi, Walid & Al-Yahyaee, Khamis Hamed & Al-Jarrah, Idries Mohammad Wanas & Vo, Xuan Vinh & Kang, Sang Hoon, 2020. "Dynamic volatility transmission and portfolio management across major cryptocurrencies: Evidence from hourly data," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    2. Adekoya, Oluwasegun B. & Oliyide, Johnson A. & Oduyemi, Gabriel O., 2021. "How COVID-19 upturns the hedging potentials of gold against oil and stock markets risks: Nonlinear evidences through threshold regression and markov-regime switching models," Resources Policy, Elsevier, vol. 70(C).
    3. Pham, Linh & Karim, Sitara & Naeem, Muhammad Abubakr & Long, Cheng, 2022. "A tale of two tails among carbon prices, green and non-green cryptocurrencies," International Review of Financial Analysis, Elsevier, vol. 82(C).
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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