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Abstract

We reconsider a formula for arbitrary moments of expected discounted dividend payments in a spectrally negative Lévy risk model that was obtained in Renaud and Zhou (2007, [4]) and in Kyprianou and Palmowski (2007, [3]) and extend the result to stationary Markov processes that are skip-free upwards.

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References

  1. Gerber, H.U., Lin, S., Yang, H. A note on the dividends-penalty identity and the optimal dividend barrier. Astin Bulletin, 36(2): 489–503 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  2. Gerber, H.U., Shiu, E.S.W. Reply to Discussions on “Optimal Dividends: Analysis with Brownian Motion”. North American Actuarial Journal, 8(2): 113–115 (2004)

    Google Scholar 

  3. Kyprianou, A., Palmowski, Z. Distributional study of De Finetti’s dividend problem for a general Lévy insurance risk process. Journal of Applied Probability, 44: 428–443 (2007)

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  4. Renaud, J., Zhou, X. Distribution of the present value of dividend payments in a Lévy risk model. Journal of Applied Probability, 44: 420–427 (2007)

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Hansjörg Albrecher.

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Supported by the Swiss National Science Foundation Project (No. 200021-124635/1).

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Albrecher, H., Gerber, H.U. A note on moments of dividends. Acta Math. Appl. Sin. Engl. Ser. 27, 353–354 (2011). https://doi.org/10.1007/s10255-011-0074-x

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  • DOI: https://doi.org/10.1007/s10255-011-0074-x

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