$L^p$ optimal prediction of the last zero of a spectrally negative Lévy process

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Abstract

Given a spectrally negative L\'evy process $X$ drifting to infinity, (inspired on the early ideas of Shiryaev (2002)) we are interested in finding a stopping time that minimises the $L^p$ distance ($p>1$) with $g$, the last time $X$ is negative. The solution is substantially more difficult compared to the case $p=1$, for which it was shown by Baurdoux and Pedraza (2020) that it is optimal to stop as soon as $X$ exceeds a constant barrier. In the case of $p>1$ treated here, we prove that solving this optimal prediction problem is equivalent to solving an optimal stopping problem in terms of a two-dimensional strong Markov process that incorporates the length of the current positive excursion away from $0$. We show that an optimal stopping time is now given by the first time that $X$ exceeds a non-increasing and non-negative curve depending on the length of the current positive excursion away from $0$. We further characterise the optimal boundary and the value function as the unique solution of a non-linear system of integral equations within a subclass of functions. As examples, the case of a Brownian motion with drift and a Brownian motion with drift perturbed by a Poisson process with exponential jumps are considered.
Original languageEnglish
Pages (from-to)1350–1402
Number of pages53
JournalAnnals of Applied Probability
Volume34
Issue number1B
Publication statusPublished - 3 Feb 2024

Keywords

  • L{\'{e}}vy processes
  • optimal prediction
  • optimal stopping

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