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Parameter Estimation Methods of Required Rate of Return

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  • Battulga Gankhuu

Abstract

In this study, we introduce new estimation methods for the required rate of returns on equity and liabilities of private and public companies using the stochastic dividend discount model (DDM). To estimate the required rate of return on equity, we use the maximum likelihood method, the Bayesian method, and the Kalman filtering. We also provide a method that evaluates the market values of liabilities. We apply the model to a set of firms from the S\&P 500 index using historical dividend and price data over a 32--year period. Overall, the suggested methods can be used to estimate the required rate of returns.

Suggested Citation

  • Battulga Gankhuu, 2023. "Parameter Estimation Methods of Required Rate of Return," Papers 2305.19708, arXiv.org, revised Aug 2023.
    • Handle: RePEc:arx:papers:2305.19708
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    References listed on IDEAS

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    1. Arianna Agosto & Alessandra Mainini & Enrico Moretto, 2019. "Stochastic dividend discount model: covariance of random stock prices," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(3), pages 552-568, July.
    2. Guglielmo D'Amico & Riccardo De Blasis, 2020. "A multivariate Markov chain stock model," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2020(4), pages 272-291, April.
    3. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    4. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    5. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    6. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    7. 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.
    8. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    9. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    10. 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.
    11. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
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