IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i9p388-d1469491.html
   My bibliography  Save this article

Application of a Robust Maximum Diversified Portfolio to a Small Economy’s Stock Market: An Application to Fiji’s South Pacific Stock Exchange

Author

Listed:
  • Ronald Ravinesh Kumar

    (Department of Economics and Finance, The Business School, RMIT University, Saigon South Campus, Ho Chi Minh City 700000, Vietnam)

  • Hossein Ghanbari

    (School of Industrial Engineering, Iran University of Science & Technology, Tehran 13114-16846, Iran)

  • Peter Josef Stauvermann

    (Department of Global Business and Economics, Changwon National University, Changwon 51140, Republic of Korea)

Abstract

In this study, we apply a novel approach of portfolio diversification—the robust maximum diversified (RMD)—to a small and developing economy’s stock market. Using monthly returns data from August 2019 to May 2024 of 18/19 stocks listed on Fiji’s South Pacific Stock Exchange (SPX), we construct the RMD portfolio and simulate with additional constraints. To implement the RMD portfolio, we replace the covariance matrix with a matrix comprising unexplained variations. The RMD procedure diversifies weights, and not risks, hence we need to run a pairwise regression between two assets (stocks) and extract the R-square to create a P-matrix. We compute each asset’s beta using the market-weighted price index, and the CAPM to calculate market-adjusted returns. Next, together with other benchmark portfolios (1/N, minimum variance, market portfolio, semi-variance, maximum skewness, and the most diversified portfolio), we examine the expected returns against the risk-free (RF) rate. From the simulations, in terms of expected return, we note that eight portfolios perform up to the RF rate. Specifically, for returns between 4 and 5%, we find that max. RMD with positive Sharpe and Sortino (as constraints) and the most diversified portfolio offer comparable returns, although the latter has slightly lower standard deviation and downside volatility and contains 94% of all the stocks. Portfolios with returns between 5% and the RF rate are the minimum-variance, the semi-variance, and the max. RMD with positive Sharpe; the latter coincides with the RF rate and contains the most (94%) stocks compared to the other two. An investor with a diversification objective, some risk tolerance and return preference up to the RF rate can consider the max. RMD with positive Sharpe. However, depending on the level of risk-averseness, the minimum-variance or the semi-variance portfolio can be considered, with the latter having lower downside volatility. Two portfolios offer returns above the RF rate—the market portfolio (max. Sharpe) and the maximum Sortino. Although the latter has the highest return, this portfolio is the least diversified and has the largest standard deviation and downside volatility. To achieve diversification and returns above the RF rate, the market portfolio should be considered.

Suggested Citation

  • Ronald Ravinesh Kumar & Hossein Ghanbari & Peter Josef Stauvermann, 2024. "Application of a Robust Maximum Diversified Portfolio to a Small Economy’s Stock Market: An Application to Fiji’s South Pacific Stock Exchange," JRFM, MDPI, vol. 17(9), pages 1-30, September.
  • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:9:p:388-:d:1469491
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/9/388/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/17/9/388/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gilles Boevi Koumou, 2020. "Diversification and portfolio theory: a review," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(3), pages 267-312, September.
    2. Righi, Marcelo Brutti & Borenstein, Denis, 2018. "A simulation comparison of risk measures for portfolio optimization," Finance Research Letters, Elsevier, vol. 24(C), pages 105-112.
    3. Salo, Ahti & Doumpos, Michalis & Liesiö, Juuso & Zopounidis, Constantin, 2024. "Fifty years of portfolio optimization," European Journal of Operational Research, Elsevier, vol. 318(1), pages 1-18.
    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. Maziar Sahamkhadam, 2021. "Dynamic copula-based expectile portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 209-223, May.
    2. Benoît Carmichael & Gilles Boevi Koumou & Kevin Moran, 2021. "The RQE-CAPM : New insights about the pricing of idiosyncratic risk," CIRANO Working Papers 2021s-28, CIRANO.
    3. Benoît Carmichael & Gilles Boevi Koumou & Kevin Moran, 2023. "Unifying Portfolio Diversification Measures Using Rao’s Quadratic Entropy," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(4), pages 769-802, December.
    4. Fracasso, Laís Martins & Müller, Fernanda Maria & Ramos, Henrique Pinto & Righi, Marcelo Brutti, 2023. "Is there a risk premium? Evidence from thirteen measures," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 182-199.
    5. Nguyen, An Pham Ngoc & Mai, Tai Tan & Bezbradica, Marija & Crane, Martin, 2023. "Volatility and returns connectedness in cryptocurrency markets: Insights from graph-based methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    6. Vukovic, Darko B. & Maiti, Moinak & Frömmel, Michael, 2022. "Inflation and portfolio selection," Finance Research Letters, Elsevier, vol. 50(C).
    7. Yue Liu & Hao Dong & Pierre Failler, 2019. "The Oil Market Reactions to OPEC’s Announcements," Energies, MDPI, vol. 12(17), pages 1-15, August.
    8. Marcelo Brutti Righi & Marlon Ruoso Moresco, 2022. "Star-Shaped deviations," Papers 2207.08613, arXiv.org.
    9. Sant’Anna, Leonardo Riegel & Righi, Marcelo Brutti & Müller, Fernanda Maria & Guedes, Pablo Cristini, 2022. "Risk measure index tracking model," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 361-383.
    10. Righi, Marcelo Brutti & Müller, Fernanda Maria & Moresco, Marlon Ruoso, 2020. "On a robust risk measurement approach for capital determination errors minimization," Insurance: Mathematics and Economics, Elsevier, vol. 95(C), pages 199-211.
    11. Li Guo & Wolfgang Karl Härdle & Yubo Tao, 2024. "A Time-Varying Network for Cryptocurrencies," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 437-456, April.
    12. Zaremba, Adam, 2019. "Price range and the cross-section of expected country and industry returns," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 174-189.
    13. Diana Barro & Antonella Basso & Stefania Funari & Guglielmo Alessandro Visentin, 2024. "The Effects of the Introduction of Volume-Based Liquidity Constraints in Portfolio Optimization with Alternative Investments," Mathematics, MDPI, vol. 12(15), pages 1-26, August.
    14. Benoit Carmichael & Gilles Boevi Koumou & Kevin Moran, 2021. "The political reception of innovations," Cahiers de recherche 2107, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    15. Marcelo Brutti Righi & Fernanda Maria Muller & Marlon Ruoso Moresco, 2022. "A risk measurement approach from risk-averse stochastic optimization of score functions," Papers 2208.14809, arXiv.org, revised May 2023.
    16. Alqaralleh, Huthaifa & Canepa, Alessandra, 2022. "The role of precious metals in portfolio diversification during the Covid19 pandemic: A wavelet-based quantile approach," Resources Policy, Elsevier, vol. 75(C).
    17. Alim, Wajid & Ali, Amjad, 2021. "The Impact of Islamic Portfolio on Risk and Return," MPRA Paper 111048, University Library of Munich, Germany.
    18. Gilles Boevi Koumou, 2023. "Risk budgeting using a generalized diversity index," Journal of Asset Management, Palgrave Macmillan, vol. 24(6), pages 443-458, October.
    19. Salih Çam, 2023. "Asset Allocation with Combined Models Based on Game-Theory Approach and Markov Chain Models," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 26-36, December.
    20. Woźny, Jakub & Jaworski, Piotr & Jelito, Damian & Pitera, Marcin & Wyłomańska, Agnieszka, 2025. "Gaussian dependence structure pairwise goodness-of-fit testing based on conditional covariance and the 20/60/20 rule," Journal of Multivariate Analysis, Elsevier, vol. 206(C).

    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:9:p:388-:d:1469491. 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.