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Copula Modelling on the Dynamic Dependence Structure of Multiple Air Pollutant Variables

Author

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  • Nurulkamal Masseran

    (Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia)

  • Saiful Izzuan Hussain

    (Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia)

Abstract

A correlation analysis of pollutant variables provides comprehensive information on dependency behaviour and is thus useful in relating the risk and consequences of pollution events. However, common correlation measurements fail to capture the various properties of air pollution data, such as their non-normal distribution, heavy tails, and dynamic changes over time. Hence, they cannot generate highly accurate information. To overcome this issue, this study proposes a combination of the Generalized Autoregressive Conditional Heteroskedasticity model, Generalized Pareto distribution, and stochastic copulas as a tool to investigate the dependence structure between the PM 10 variable and other pollutant variables, including CO, NO 2 , O 3 , and SO 2 . Results indicate that the dynamic dependence structure between PM 10 and other pollutant variables can be described with a ranking of PM 10 –CO > PM 10 –SO 2 > PM 10 –NO 2 > PM 10 –O 3 for the overall time paths ( δ ) and the upper tail ( τ U ) or lower tail ( τ L ) dependency measures. This study reveals an evident correlation among pollutant variables that changes over time; such correlation reflects dynamic dependency.

Suggested Citation

  • Nurulkamal Masseran & Saiful Izzuan Hussain, 2020. "Copula Modelling on the Dynamic Dependence Structure of Multiple Air Pollutant Variables," Mathematics, MDPI, vol. 8(11), pages 1-15, October.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:1910-:d:438415
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Nurulkamal Masseran & Muhammad Aslam Mohd Safari, 2021. "Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events," IJERPH, MDPI, vol. 18(13), pages 1-17, June.
    2. Mohd Sabri Ismail & Nurulkamal Masseran & Mohd Almie Alias & Sakhinah Abu Bakar, 2024. "Modeling Asymmetric Dependence Structure of Air Pollution Characteristics: A Vine Copula Approach," Mathematics, MDPI, vol. 12(4), pages 1-23, February.
    3. Nurulkamal Masseran, 2022. "Multifractal Characteristics on Temporal Maximum of Air Pollution Series," Mathematics, MDPI, vol. 10(20), pages 1-15, October.
    4. Hussain, Saiful Izzuan & Nur-Firyal, R. & Ruza, Nadiah, 2022. "Linkage transitions between oil and the stock markets of countries with the highest COVID-19 cases," Journal of Commodity Markets, Elsevier, vol. 28(C).
    5. Nurulkamal Masseran & Muhammad Aslam Mohd Safari, 2022. "Statistical Modeling on the Severity of Unhealthy Air Pollution Events in Malaysia," Mathematics, MDPI, vol. 10(16), pages 1-15, August.
    6. Nurulkamal Masseran, 2021. "Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach," IJERPH, MDPI, vol. 18(16), pages 1-18, August.
    7. Zhang, Jiao & Li, Youping & Liu, Chunqiong & Wu, Bo & Shi, Kai, 2022. "A study of cross-correlations between PM2.5 and O3 based on Copula and Multifractal methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    8. Amjad, Muhammad & Akbar, Muhammad & Ullah, Hamd, 2022. "A copula-based approach for creating an index of micronutrient intakes at household level in Pakistan," Economics & Human Biology, Elsevier, vol. 46(C).

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