IDEAS home Printed from https://ideas.repec.org/a/cup/jfinqa/v31y1996i04p605-631_02.html
   My bibliography  Save this article

On Estimating the Expected Rate of Return in Diffusion Price Models with Application to Estimating the Expected Return on the Market

Author

Listed:
  • Goldenberg, David H.
  • Schmidt, Raymond J.

Abstract

This paper derives and numerically simulates maximum likelihood estimators for the drift in several important diffusion price models. The time series convergence properties of these estimators are compared to those of standard estimators including the geometric and arithmetic means. Merton (1980) demonstrated that it is difficult to efficiently estimate the drift in a log-normal diffusion model. We qualify and strengthen his result by noting that his estimator is the maximum likelihood estimator and by applying our simulation results. However, we also demonstrate that it is possible to efficiently estimate the drift in other useful diffusion price models. In particular, by asking just how much time is needed in order for the maximum likelihood estimators of the drift in different diffusion processes to converge, these results qualify and quantify Black's (1993) statement that “we need such a long period to estimate the average that we have little hope of seeing changes in expected return."

Suggested Citation

  • Goldenberg, David H. & Schmidt, Raymond J., 1996. "On Estimating the Expected Rate of Return in Diffusion Price Models with Application to Estimating the Expected Return on the Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 31(4), pages 605-631, December.
    • Handle: RePEc:cup:jfinqa:v:31:y:1996:i:04:p:605-631_02
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0022109000023826/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Braselton, James & Rafter, John & Humphrey, Patricia & Abell, Martha, 1999. "Randomly walking through Wall Street," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 49(4), pages 297-318.
    2. Yue Fang, 2000. "When Should Time be Continuous? Volatility Modeling and Estimation of High-Frequency Data," Econometric Society World Congress 2000 Contributed Papers 0843, Econometric Society.
    3. Basso, A. & Pianca, P., 1999. "A more informative estimation procedure for the parameters of a diffusion process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 269(1), pages 45-53.

    More about this item

    Statistics

    Access and download statistics

    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:cup:jfinqa:v:31:y:1996:i:04:p:605-631_02. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/jfq .

    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.