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Volatility Transmission in Oil Futures Markets and Carbon Emissions Futures

Author

Listed:
  • Tanattrin Bunnag

    (Faculty of Science and Social Sciences, Burapha University, Thailand.)

Abstract
This paper examined the oil futures and the carbon emissions futures volatility comovements and spillovers for crude oil, gasoline and heat oil as well as carbon emissions. The data used in this study was the daily data from 2009 to 2014. The three multivariate GARCH models, namely the vector autoregression model (VAR) (3)-diagonal VECH, the VAR (3)-diagonal Baba, Engle, Kraft and Kroner (BEKK) and the VAR (3)- constant conditional correlations (CCC), were employed. The empirical results showed that the estimates of the VAR (3)-diagonal VECH and the VAR (3)-CCC parameters were statistically significant in a case involving oil except in the case of carbon emissions. This indicates that the short run persistence of shocks on the dynamic conditional correlations was greatest for RGASOLINE with RHEATOIL, while the largest long run persistence of shocks to the conditional correlations for RCRUDE with RGASOLINE. At the same time the VAR (3)-diagonal BEKK parameters were statistically significant in all cases. This indicates that the short run persistence of shocks on the dynamic conditional correlations is greatest for RHEATOIL with RCO2, while the largest long run persistence of shocks to the conditional correlations for RCRUDE with RCO2 and RHEATOIL with RCO2. Finally, we would choose the best model next by considering the value of log-likelihood, Akaike information criterion, Schwarz information criterion and Hannan-Quinn information criterion. The value of these figures, it could be concluded that we should choose the VAR (3)-diagonal BEKK model in volatility analysis of the oil futures and the carbon emissions futures returns. In addition, we could conclude that oil futures volatility having an impact on carbon emissions futures volatility.

Suggested Citation

  • Tanattrin Bunnag, 2015. "Volatility Transmission in Oil Futures Markets and Carbon Emissions Futures," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 647-659.
    • Handle: RePEc:eco:journ2:2015-03-02
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Ayben Koy & G l Okay, 2020. "Are Carbon Leader Indexes Related with Carbon Prices under Different Regimes?," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 115-121.
    2. Tanattrin Bunnag, 2016. "Volatility Transmission in Crude Oil, Gold, Standard and Poor s 500 and US Dollar Index Futures using Vector Autoregressive Multivariate Generalized Autoregressive Conditional Heteroskedasticity Model," International Journal of Energy Economics and Policy, Econjournals, vol. 6(1), pages 39-52.
    3. K. Abhaya Kumar & Prakash Pinto & Iqbal Thonse Hawaldar & Saheem Shaikh & Shravan Bhagav & B. Padmanabha, 2022. "Investigating the Nexus between Crude Oil Price and Stock Prices of Oil Exploration Companies," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 40-47, July.
    4. Samet G nay, 2015. "Markov Regime Switching Generalized Autoregressive Conditional Heteroskedastic Model and Volatility Modeling for Oil Returns," International Journal of Energy Economics and Policy, Econjournals, vol. 5(4), pages 979-985.
    5. Xu, Yingying & Shao, Xuefeng & Tanasescu, Cristina, 2024. "How are artificial intelligence, carbon market, and energy sector connected? A systematic analysis of time-frequency spillovers," Energy Economics, Elsevier, vol. 132(C).

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    More about this item

    Keywords

    The Oil Futures and the Carbon Emissions Futures Volatility; Comovements and Spillovers; Multivariate GARCH Models;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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