Predicting the last zero of a spectrally negative L\'evy process

TL;DR

This paper addresses the problem of predicting the last zero of a spectrally negative Lévy process by formulating it as an optimal stopping problem and characterizing the optimal stopping time.

Contribution

It introduces a novel approach to predict the last zero of a Lévy process using a median-based stopping rule and characterizes conditions for fit continuity.

Findings

Optimal stopping time is the first passage above a median-based level.

The stopping rule is explicitly characterized in terms of the process distribution.

Conditions for continuous and smooth fit are established.

Abstract

Last passage times arise in a number of areas of applied probability, including risk theory and degradation models. Such times are obviously not stopping times since they depend on the whole path of the underlying process. We consider the problem of finding a stopping time that minimises the $L^1$-distance to the last time a spectrally negative L\'evy process $X$ is below zero. Examples of related problems in a finite horizon setting for processes with continuous paths are Du Toit et al. (2008) and Glover and Hulley (2014), where the last zero is predicted for a Brownian motion with drift, and for a transient diffusion, respectively. As we consider the infinite horizon setting, the problem is interesting only when the L\'evy process drifts to $\infty$ which we will assume throughout. Existing results allow us to rewrite the problem as a classic optimal stopping problem, i.e. with an adapted payoff process. We use a direct method to show that an optimal stopping time is given by the first passage time above a level defined in terms of the median of the convolution with itself of the distribution function of $-\inf_{t\geq 0}X_t$. We also characterise when continuous and/or smooth fit holds.