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The Volatility Assessment of CO2 Emissions in Uzbekistan: ARCH/GARCH Models

Author

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  • Bekhzod Kuziboev

    (Department of Economics, Urgench State University, Urgench, Uzbekistan; & Department of Trade, Tourism and Languages, Faculty of Economics, University of South Bohemia, Czech Republic,)

  • Petra VysuÅ¡ilová

    (Department of Trade, Tourism and Languages, Faculty of Economics, University of South Bohemia, Czech Republic,)

  • Raufhon Salahodjaev

    (Central Asian University, Tashkent 111221, Uzbekistan.)

  • Alibek Rajabov

    (Department of Economics, Urgench State University, Urgench, Uzbekistan,)

  • Tukhtabek Rakhimov

    (Department of Economics, Urgench State University, Urgench, Uzbekistan,)

Abstract

The study is pioneer to investigate the volatility of CO2 emissions in Uzbekistan. To this end, ARCH (Autoregressive Conditional Heteroskedasticity) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models are used spanning the period 1925-2021 for the annual data of CO2 emissions. The results indicate that ARCH model is more adequate that GARCH model in the volatility assessment. Furthermore, it is found that the volatility of CO2 emissions in Uzbekistan is very high. The policymakers have to consider the high volatility of CO2 emissions in the environmental policy measures dedicated to reduce carbon dioxide emissions.

Suggested Citation

  • Bekhzod Kuziboev & Petra VysuÅ¡ilová & Raufhon Salahodjaev & Alibek Rajabov & Tukhtabek Rakhimov, 2023. "The Volatility Assessment of CO2 Emissions in Uzbekistan: ARCH/GARCH Models," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 1-7, September.
    • Handle: RePEc:eco:journ2:2023-05-1
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    References listed on IDEAS

    as
    1. Benz, Eva & Trück, Stefan, 2009. "Modeling the price dynamics of CO2 emission allowances," Energy Economics, Elsevier, vol. 31(1), pages 4-15, January.
    2. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Bekhzod Kuziboev & Ergash Ibadullaev & Olimjon Saidmamatov & Alibek Rajabov & Peter Marty & Sherzodbek Ruzmetov & Alisher Sherov, 2023. "The Role of Renewable Energy and Human Capital in Reducing Environmental Degradation in Europe and Central Asia: Panel Quantile Regression and GMM Approach," Energies, MDPI, vol. 16(22), pages 1-12, November.

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    More about this item

    Keywords

    CO2 Emissions; Volatility; ARCH; GARCH; Uzbekistan;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

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