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Minimizing the Probability of Lifetime Exponential Parisian Ruin

  • Xiaoqing Liang
  • Virginia R. YoungEmail author
Article
  • 97 Downloads

Abstract

We find the optimal investment strategy in a Black–Scholes market to minimize the probability of so-called lifetime exponential Parisian ruin, that is, the probability that wealth exhibits an excursion below zero of an exponentially distributed time before the individual dies. We find that leveraging the risky asset is worse for negative wealth when minimizing the probability of lifetime exponential Parisian ruin than when minimizing the probability of lifetime ruin. Moreover, when wealth is negative, the optimal amount invested in the risky asset increases as the hazard rate of the exponential “excursion clock” increases. In view of the heavy leveraging when wealth is negative, we also compute the minimum probability of lifetime exponential Parisian ruin under a constraint on investment. Finally, we derive an asymptotic expansion of the minimum probability of lifetime exponential Parisian ruin for small values of the hazard rate of the excursion clock. It is interesting to find that for small values of this hazard rate, the minimum probability of lifetime exponential Parisian ruin is proportional to the minimum occupation time studied in Bayraktar and Young, and the proportion equals the hazard rate. To the best of our knowledge, our work is the first to control the probability of Parisian ruin.

Keywords

Exponential Parisian ruin Optimal investment Stochastic control 

Mathematics Subject Classification

93E20 91B30 49K10 49L20 

Notes

Acknowledgements

X. Liang thanks the National Natural Science Foundation of China (11701139, 11571189) and the Natural Science Foundation of Hebei Province (A2018202057) for financial support of her research. V.R. Young thanks the Cecil J. and Ethel M. Nesbitt Professorship for financial support of her research.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Hebei University of TechnologyTianjinChina
  2. 2.University of MichiganAnn ArborUS

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