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Dividends And Compound Poisson Processes: A New Stochastic Stock Price Model

Author

Listed:
  • BATTULGA GANKHUU

    (Department of Applied Mathematics, National University of Mongolia, Ikh Surguuliin Gudamj, Ulaanbaatar 14201, Mongolia)

  • JACOB KLEINOW

    (zeb Consulting, Friedrichstraße 78, D-10117 Berlin, Germany)

  • ALTANGEREL LKHAMSUREN

    (Faculty of Mathematics, Computer and Natural Sciences, German-Mongolian Institute for Resources and Technology, GMIT Campus, 2nd Khoroo, Nalaikh 12790, Mongolia)

  • ANDREAS HORSCH

    (Faculty of Business Administration, Technische Universität Bergakademie Freiberg, Schlossplatz 1, D-09599 Freiberg, Germany)

Abstract

This study introduces a stochastic multi-period dividend discount model (DDM) that includes (i) a compound nonhomogenous Poisson process for dividend growth and (ii) the probability of firm default. We obtain maximum likelihood (ML) estimators and confidence interval formulas of our model parameters. We apply the model to a set of firms from the S&P 500 index using historical dividend and price data over a 42-year period. Interestingly, stock price estimations calculated with the model are close to the observable prices. Overall, we prove that the model can be a useful tool for stock pricing.

Suggested Citation

  • Battulga Gankhuu & Jacob Kleinow & Altangerel Lkhamsuren & Andreas Horsch, 2022. "Dividends And Compound Poisson Processes: A New Stochastic Stock Price Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 25(03), pages 1-36, May.
    • Handle: RePEc:wsi:ijtafx:v:25:y:2022:i:03:n:s0219024922500145
    DOI: 10.1142/S0219024922500145
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    Cited by:

    1. Battulga Gankhuu, 2023. "Parameter Estimation Methods of Required Rate of Return," Papers 2305.19708, arXiv.org, revised Aug 2023.

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