IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i1p35-d1319881.html
   My bibliography  Save this article

The Financial Market of Indices of Socioeconomic Well-Being

Author

Listed:
  • Thilini V. Mahanama

    (Department of Industrial Management, University of Kelaniya, Kelaniya 11600, Sri Lanka)

  • Abootaleb Shirvani

    (Department of Mathematical Science, Kean University, Union, NJ 07083, USA)

  • Svetlozar Rachev

    (Department of Mathematics & Statistics, Texas Tech University, Lubbock, TX 79409, USA)

  • Frank J. Fabozzi

    (Carey Business School, Johns Hopkins University, Baltimore, MD 21218, USA)

Abstract

This study discusses how financial economic theory and its quantitative tools can be applied to create socioeconomic indices and develop a financial market for the so-called “socioeconomic well-being indices”. In this study, we quantify socioeconomic well-being by assigning a dollar value to the well-being factors of selected countries; this is analogous to how the Dow 30 encapsulates the financial health of the US market. While environmental, social, and governance (ESG) financial markets address socioeconomic issues, our focus is broader, encompassing national citizens’ well-being. The dollar-denominated socioeconomic indices for each country can be viewed as financial assets that can serve as risky assets for constructing a global index, which, in turn, serves as a “market of well-being socioeconomic index”. This novel global index of well-being, paralleling the Dow Jones Industrial Average (DJIA), provides a comprehensive representation of the world’s socioeconomic status. Through advanced financial econometrics and dynamic asset pricing methodologies, we evaluate the potential for significant downturns in both the socioeconomic well-being indices of individual countries and the aggregate global index. This innovative approach allows us to engineer financial instruments akin to portfolio insurance, such as index puts, designed to hedge against these downturn risks. Our findings propose a financial market model for well-being indices, encouraging the financial industry to adopt and trade these indices as mechanisms to manage and hedge against downturn risks in well-being.

Suggested Citation

  • Thilini V. Mahanama & Abootaleb Shirvani & Svetlozar Rachev & Frank J. Fabozzi, 2024. "The Financial Market of Indices of Socioeconomic Well-Being," JRFM, MDPI, vol. 17(1), pages 1-19, January.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:1:p:35-:d:1319881
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/1/35/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/1/35/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tor Jacobson & Jesper Lindé & Kasper Roszbach, 2005. "Credit Risk Versus Capital Requirements under Basel II: Are SME Loans and Retail Credit Really Different?," Journal of Financial Services Research, Springer;Western Finance Association, vol. 28(1), pages 43-75, October.
    2. Girardi, Giulio & Tolga Ergün, A., 2013. "Systemic risk measurement: Multivariate GARCH estimation of CoVaR," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3169-3180.
    3. Giovanni Barone Adesi & Robert F. Engle & Loriano Mancini, 2014. "A GARCH Option Pricing Model with Filtered Historical Simulation," Palgrave Macmillan Books, in: Giovanni Barone Adesi (ed.), Simulating Security Returns: A Filtered Historical Simulation Approach, chapter 4, pages 66-108, Palgrave Macmillan.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    6. Carlo Acerbi & Dirk Tasche, 2002. "Expected Shortfall: A Natural Coherent Alternative to Value at Risk," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 31(2), pages 379-388, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thilini V. Mahanama & Abootaleb Shirvani & Svetlozar Rachev, 2023. "The Financial Market of Indices of Socioeconomic Wellbeing," Papers 2303.05654, arXiv.org.
    2. Thisari K. Mahanama & Abootaleb Shirvani & Svetlozar Rachev & Frank J. Fabozzi, 2023. "The Financial Market of Environmental Indices," Papers 2308.15661, arXiv.org.
    3. Thilini Mahanama & Abootaleb Shirvani & Svetlozar Rachev, 2022. "A Natural Disasters Index," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 24(2), pages 263-284, April.
    4. Robert F. Engle & Emil N. Siriwardane, 2018. "Structural GARCH: The Volatility-Leverage Connection," The Review of Financial Studies, Society for Financial Studies, vol. 31(2), pages 449-492.
    5. Ahmed, Mohamed S. & Alhadab, Mohammad, 2020. "Momentum, asymmetric volatility and idiosyncratic risk-momentum relation: Does technology-sector matter?," The Quarterly Review of Economics and Finance, Elsevier, vol. 78(C), pages 355-371.
    6. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022. "Next generation models for portfolio risk management: An approach using financial big data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
    7. John García & Francesc Trillas, 2011. "Control corporativo y riqueza de los accionistas en el sector eléctrico europeo (2000-2007)," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 13(25), pages 297-319, July-Dece.
    8. Rui Zhou & Johnny Siu-Hang Li & Jeffrey Pai, 2019. "Pricing temperature derivatives with a filtered historical simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(15), pages 1462-1484, October.
    9. Seyoung Park & Eun Ryung Lee & Sungchul Lee & Geonwoo Kim, 2019. "Dantzig Type Optimization Method with Applications to Portfolio Selection," Sustainability, MDPI, vol. 11(11), pages 1-32, June.
    10. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
    11. Kei Nakagawa & Yusuke Uchiyama, 2020. "GO-GJRSK Model with Application to Higher Order Risk-Based Portfolio," Mathematics, MDPI, vol. 8(11), pages 1-12, November.
    12. Turan G. Bali & Hao Zhou, 2011. "Risk, uncertainty, and expected returns," Finance and Economics Discussion Series 2011-45, Board of Governors of the Federal Reserve System (U.S.).
    13. Konstantinos Drakos, 2009. "Cross-Country Stock Market Reactions to Major Terror Events: The Role of Risk Perception," Economics of Security Working Paper Series 16, DIW Berlin, German Institute for Economic Research.
    14. Campbell, John Y. & Giglio, Stefano & Polk, Christopher & Turley, Robert, 2018. "An intertemporal CAPM with stochastic volatility," Journal of Financial Economics, Elsevier, vol. 128(2), pages 207-233.
    15. Chao Wang & Richard Gerlach, 2021. "A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting," Papers 2106.00288, arXiv.org, revised Oct 2022.
    16. Peter Christoffersen & Ruslan Goyenko & Kris Jacobs & Mehdi Karoui, 2018. "Illiquidity Premia in the Equity Options Market," The Review of Financial Studies, Society for Financial Studies, vol. 31(3), pages 811-851.
    17. Mozumder, Sharif & Frijns, Bart & Talukdar, Bakhtear & Kabir, M. Humayun, 2024. "On practitioners closed-form GARCH option pricing," International Review of Financial Analysis, Elsevier, vol. 94(C).
    18. Chalamandaris, Georgios & Rompolis, Leonidas S., 2012. "Exploring the role of the realized return distribution in the formation of the implied volatility smile," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1028-1044.
    19. Ioannis D Vrontos & Loukia Meligkotsidou & Spyridon D Vrontos, 2011. "Performance evaluation of mutual fund investments: The impact of non-normality and time-varying volatility," Journal of Asset Management, Palgrave Macmillan, vol. 12(4), pages 292-307, September.
    20. Zhao, Zifeng & Zhang, Zhengjun & Chen, Rong, 2018. "Modeling maxima with autoregressive conditional Fréchet model," Journal of Econometrics, Elsevier, vol. 207(2), pages 325-351.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:17:y:2024:i:1:p:35-:d:1319881. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.