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Dividend based risk measures: A Markov chain approach

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  • D'Amico, Guglielmo
  • De Blasis, Riccardo

Abstract

Computations of risk measures in the context of the dividend valuation model is a crucial aspect to deal with when investors decide to buy a share of common stock. This is achieved by using a Markov chain model of growth-dividend evolution, imposing an assumption that controls the growth of the dividend process and in turn allows for the computation of the moments of the price process and the fulfillment of a set of transversality conditions which allows avoiding the presence of speculative bubbles in the market. The probability distribution of the fundamental value of the stock is recovered by solving a moment problem, based on the solution of a maximum-entropy approach from which it is possible to compute classical risk measures based on these fundamental variables. The methodology is applied to real dividend data from the S&P 500 index. Results show that our model provides complete information about the fundamental price not limited to its expectation.

Suggested Citation

  • D'Amico, Guglielmo & De Blasis, Riccardo, 2024. "Dividend based risk measures: A Markov chain approach," Applied Mathematics and Computation, Elsevier, vol. 471(C).
    • Handle: RePEc:eee:apmaco:v:471:y:2024:i:c:s0096300324000833
    DOI: 10.1016/j.amc.2024.128611
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    References listed on IDEAS

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    1. Arianna Agosto & Enrico Moretto, 2015. "Variance matters (in stochastic dividend discount models)," Annals of Finance, Springer, vol. 11(2), pages 283-295, May.
    2. Guglielmo D'Amico & Riccardo De Blasis, 2020. "A review of the Dividend Discount Model: from deterministic to stochastic models," Papers 2001.00465, arXiv.org.
    3. Olivier J. Blanchard & Mark W. Watson, 1982. "Bubbles, Rational Expectations and Financial Markets," NBER Working Papers 0945, National Bureau of Economic Research, Inc.
    4. 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.
    5. 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.
    6. Sergio Ortobelli & Noureddine Kouaissah & Tomáš Tichý, 2017. "On the impact of conditional expectation estimators in portfolio theory," Computational Management Science, Springer, vol. 14(4), pages 535-557, October.
    7. Brooks, Robert & Helms, Billy, 1990. "An N-Stage, Fractional Period, Quarterly Dividend Discount Model," The Financial Review, Eastern Finance Association, vol. 25(4), pages 651-657, November.
    8. Lettau, Martin & Ludvigson, Sydney C., 2005. "Expected returns and expected dividend growth," Journal of Financial Economics, Elsevier, vol. 76(3), pages 583-626, June.
    9. Giner, Javier & Zakamulin, Valeriy, 2023. "A regime-switching model of stock returns with momentum and mean reversion," Economic Modelling, Elsevier, vol. 122(C).
    10. 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.
    11. Vlad Stefan Barbu & Guglielmo D’Amico & Riccardo Blasis, 2017. "Novel advancements in the Markov chain stock model: analysis and inference," Annals of Finance, Springer, vol. 13(2), pages 125-152, May.
    12. Richardson, Scott & Tuna, Irem & Wysocki, Peter, 2010. "Accounting anomalies and fundamental analysis: A review of recent research advances," Journal of Accounting and Economics, Elsevier, vol. 50(2-3), pages 410-454, December.
    13. Paul A. Samuelson, 1973. "Proof That Properly Discounted Present Values of Assets Vibrate Randomly," Bell Journal of Economics, The RAND Corporation, vol. 4(2), pages 369-374, Autumn.
    14. Bartram, Söhnke M. & Grinblatt, Mark, 2018. "Agnostic fundamental analysis works," Journal of Financial Economics, Elsevier, vol. 128(1), pages 125-147.
    15. Huazhu Zhang & Cheng Yan, 2018. "Modelling fundamental analysis in portfolio selection," Quantitative Finance, Taylor & Francis Journals, vol. 18(8), pages 1315-1326, August.
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