IDEAS home Printed from https://ideas.repec.org/a/ags/reapec/143470.html
   My bibliography  Save this article

Forecasting Performance with the Harmonic Mean: Long-Term Investment Horizons in Shanghai Stock Exchange

Author

Listed:
  • Missiakoulis, Spyros
  • Vasiliou, Dimitrios
  • Eriotis, Nikolaos

Abstract

Portfolio managers favor long-term investment horizons. Their performance is usually forecasted using either the arithmetic mean or the geometric mean. The harmonic mean is generally ignored as an instrument of financial and/or portfolio management. We examine the performance of the harmonic mean employing real life data on SSE180 Index and we compare it with the corresponding performances of arithmetic and geometric means. In all cases, the harmonic mean gave us the best performance.

Suggested Citation

  • Missiakoulis, Spyros & Vasiliou, Dimitrios & Eriotis, Nikolaos, 2012. "Forecasting Performance with the Harmonic Mean: Long-Term Investment Horizons in Shanghai Stock Exchange," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 8(1), pages 1-11, May.
    • Handle: RePEc:ags:reapec:143470
    DOI: 10.22004/ag.econ.143470
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/143470/files/8-Spyros.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.143470?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ian Cooper, 1996. "Arithmetic versus geometric mean estimators: Setting discount rates for capital budgeting," European Financial Management, European Financial Management Association, vol. 2(2), pages 157-167, July.
    2. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
    3. Ercan Balaban & Asli Bayar & Robert Faff, 2006. "Forecasting stock market volatility: Further international evidence," The European Journal of Finance, Taylor & Francis Journals, vol. 12(2), pages 171-188.
    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. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    2. Balaban, Ercan & Lu, Shan, 2016. "Forecasting the term structure of volatility of crude oil price changes," Economics Letters, Elsevier, vol. 141(C), pages 116-118.
    3. Matei, Marius, 2010. "Risk analysis in the evaluation of the international investment opportunities. Advances in modelling and forecasting volatility for risk assessment purposes," Working Papers of Institute for Economic Forecasting 100201, Institute for Economic Forecasting.
    4. Antonio Rubia & Trino-Manuel Ñíguez, 2006. "Forecasting the conditional covariance matrix of a portfolio under long-run temporal dependence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 439-458.
    5. Alan Gregory, 2011. "The Expected Cost of Equity and the Expected Risk Premium in the UK," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 3(1), pages 1-26, April.
    6. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    7. Hartwell, Christopher A., 2014. "The impact of institutional volatility on financial volatility in transition economies : a GARCH family approach," BOFIT Discussion Papers 6/2014, Bank of Finland, Institute for Economies in Transition.
    8. Brooks, Robert D. & Davidson, Sinclair & Faff, Robert W., 1997. "An examination of the effects of major political change on stock market volatility: the South African experience," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 7(3), pages 255-275, October.
    9. Sang Hoon Kang & Seong-Min Yoon, 2010. "Sudden Changes and Persistence in Volatility of Korean Equity Sector Returns," Korean Economic Review, Korean Economic Association, vol. 26, pages 431-451.
    10. Ercan Balaban & Asli Bayar & Ozgur Berk Kan, 2001. "Stock returns, seasonality and asymmetric conditional volatility in world equity markets," Applied Economics Letters, Taylor & Francis Journals, vol. 8(4), pages 263-268.
    11. Mensi, Walid & Hammoudeh, Shawkat & Yoon, Seong-Min, 2014. "Structural breaks and long memory in modeling and forecasting volatility of foreign exchange markets of oil exporters: The importance of scheduled and unscheduled news announcements," International Review of Economics & Finance, Elsevier, vol. 30(C), pages 101-119.
    12. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
    13. Stéphane Yen & Ming-Hsiang Chen, 2010. "Open interest, volume, and volatility: evidence from Taiwan futures markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 34(2), pages 113-141, April.
    14. Lange, Stephen, 1999. "Modeling asset market volatility in a small market:: Accounting for non-synchronous trading effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 9(1), pages 1-18, January.
    15. Ezzat, Hassan, 2012. "The Application of GARCH and EGARCH in Modeling the Volatility of Daily Stock Returns During Massive Shocks: The Empirical Case of Egypt," MPRA Paper 50530, University Library of Munich, Germany.
    16. Chiang, Min-Hsien & Huang, Hsin-Yi, 2011. "Stock market momentum, business conditions, and GARCH option pricing models," Journal of Empirical Finance, Elsevier, vol. 18(3), pages 488-505, June.
    17. Brooks, C. & Clare, A. D. & Persand, G., 2000. "A word of caution on calculating market-based minimum capital risk requirements," Journal of Banking & Finance, Elsevier, vol. 24(10), pages 1557-1574, October.
    18. Pandey, Ajay, 2003. "Modeling and Forecasting Volatility in Indian Capital Markets," IIMA Working Papers WP2003-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    19. Manh Ha Nguyen & Olivier Darné, 2018. "Forecasting and risk management in the Vietnam Stock Exchange," Working Papers halshs-01679456, HAL.
    20. Ercan Balaban & Charalambos Th. Constantinou, 2006. "Volatility clustering and event-induced volatility: Evidence from UK mergers and acquisitions," The European Journal of Finance, Taylor & Francis Journals, vol. 12(5), pages 449-453.

    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:ags:reapec:143470. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aelinnz.html .

    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.