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Volatility Modeling and Dependence Structure of ESG and Conventional Investments

Author

Listed:
  • Joanna Górka

    (Department of Econometrics and Statistics, Nicolaus Copernicus University in Torun, 87-100 Toruń, Poland)

  • Katarzyna Kuziak

    (Department of Financial Investments and Risk Management, Wroclaw University of Economics and Business, 53-345 Wrocław, Poland)

Abstract

The question of whether environmental, social, and governance investments outperform or underperform other conventional financial investments has been debated in the literature. In this study, we compare the volatility of rates of return of selected ESG indices and conventional ones and investigate dependence between them. Analysis of tail dependence is important to evaluate the diversification benefits between conventional investments and ESG investments, which is necessary in constructing optimal portfolios. It allows investors to diversify the risk of the portfolio and positively impact the environment by investing in environmentally friendly companies. Examples of institutions that are paying attention to ESG issues are banks, which are increasingly including products that support sustainability goals in their offers. This analysis could be also important for policymakers. The European Banking Authority (EBA) has admitted that ESG factors can contribute to risk. Therefore, it is important to model and quantify it. The conditional volatility models from the GARCH family and tail-dependence coefficients from the copula-based approach are applied. The analysis period covered 2007 until 2019. The period of the COVID-19 pandemic has not been analyzed due to the relatively short time series regarding data requirements from models’ perspective. Results of the research confirm the higher dependence of extreme values in the crisis period (e.g., tail-dependence values in 2009–2014 range from 0.4820/0.4933 to 0.7039/0.6083, and from 0.5002/0.5369 to 0.7296/0.6623), and low dependence of extreme values in stabilization periods (e.g., tail-dependence values in 2017–2019 range from 0.1650 until 0.6283/0.4832, and from 0.1357 until 0.6586/0.5002). Diversification benefits vary in time, and there is a need to separately analyze crisis and stabilization periods.

Suggested Citation

  • Joanna Górka & Katarzyna Kuziak, 2022. "Volatility Modeling and Dependence Structure of ESG and Conventional Investments," Risks, MDPI, vol. 10(1), pages 1-25, January.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:1:p:20-:d:722972
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    References listed on IDEAS

    as
    1. Das, Nandita & Chatterje, Swarn & Ruf, Bernadette & Sunder, Aman, 2018. "ESG Ratings and the Performance of Socially Responsible Mutual Funds: A Panel Study," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 17(1), pages 49-57.
    2. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    3. Nandita Das & Bernadette Ruf & Swarn Chatterjee & Aman Sunder, 2018. "Fund Characteristics and Performances of Socially Responsible Mutual Funds: Do ESG Ratings Play a Role?," Papers 1806.09906, arXiv.org, revised Feb 2023.
    4. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    5. Nicholas Apergis & Vassilios Babalos & Christina Christou & Rangan Gupta, 2015. "Identifying Asymmetries between Socially Responsible and Conventional Investments," Working Papers 201537, University of Pretoria, Department of Economics.
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    Cited by:

    1. Hemendra Gupta & Rashmi Chaudhary, 2023. "An Analysis of Volatility and Risk-Adjusted Returns of ESG Indices in Developed and Emerging Economies," Risks, MDPI, vol. 11(10), pages 1-18, October.
    2. Hamzeh F. Assous, 2022. "Saudi Green Banks and Stock Return Volatility: GLE Algorithm and Neural Network Models," Economies, MDPI, vol. 10(10), pages 1-18, October.
    3. Alex Plastun & Anna Vorontsova & Inna Makarenko & Yuriy Bilan & Samer Khouri & Dalia Streimikiene, 2025. "Advantages of ESG Indexes Compared to Traditional Ones in Predicting Stock Prices," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 32(3), pages 3545-3559, May.
    4. Shahzad, Umer & Ghaemi Asl, Mahdi & Khalfaoui, Rabeh & Tedeschi, Marco, 2024. "Extreme contributions of conventional investments vis-à-vis Islamic ones to renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    5. Riccardo Savio & Edoardo D’Andrassi & Francesca Ventimiglia, 2023. "A Systematic Literature Review on ESG during the COVID-19 Pandemic," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
    6. Sandra Tenorio‐Salgueiro & Andrea Martínez‐Salgueiro & Rubén Lado‐Sestayo & Milagros Vivel‐Búa, 2026. "Does ESG Investing Pay off? Comparing the Performance of ESG and Traditional ETFs Across European and US Markets," Business Strategy and the Environment, Wiley Blackwell, vol. 35(3), pages 3561-3606, March.
    7. Staněk Gyönyör, Lucie & Horváth, Matúš, 2024. "Does ESG affect stock market dependence? An empirical exploration of S&P 1200 companies shows the divergent nature of E–S–G pillars," Research in International Business and Finance, Elsevier, vol. 69(C).

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