Predicting the ultimate supremum of a stable L\'{e}vy process with no negative jumps

TL;DR

This paper solves an optimal stopping problem for a stable Lévy process with no negative jumps, deriving explicit solutions and identifying phase transition points that differ from the Brownian motion case.

Contribution

It introduces a fractional free-boundary approach to explicitly solve the prediction problem and identifies critical parameters where optimal stopping behavior changes.

Findings

Explicit solution for the optimal stopping boundary.

Existence of critical stability index and error parameter values.

Contrast with Brownian motion case highlighting jump effects.

Abstract

Given a stable L\'{e}vy process $X=(X_t)_{0\le t\le T}$ of index $\alpha\in(1,2)$ with no negative jumps, and letting $S_t=\sup_{0\le s\le t}X_s$ denote its running supremum for $t\in [0,T]$, we consider the optimal prediction problem \[V=\inf_{0\le\tau\le T}\mathsf{E}(S_T-X_{\tau})^p,\] where the infimum is taken over all stopping times $\tau$ of $X$, and the error parameter $p\in(1,\alpha)$ is given and fixed. Reducing the optimal prediction problem to a fractional free-boundary problem of Riemann--Liouville type, and finding an explicit solution to the latter, we show that there exists $\alpha_*\in(1,2)$ (equal to 1.57 approximately) and a strictly increasing function $p_*:(\alpha_*,2)\rightarrow(1,2)$ satisfying $p_*(\alpha_*+)=1$, $p_*(2-)=2$ and $p_*(\alpha)<\alpha$ for $\alpha\in(\alpha_*,2)$ such that for every $\alpha\in (\alpha_*,2)$ and $p\in(1,p_*(\alpha))$ the following stopping time is optimal \[\tau_*=\inf\{t\in[0,T]:S_t-X_t\ge z_*(T-t)^{1/\alpha}\},\] where $z_*\in(0,\infty)$ is the unique root to a transcendental equation (with parameters $\alpha$ and $p$). Moreover, if either $\alpha\in(1,\alpha_*)$ or $p\in(p_*(\alpha),\alpha)$ then it is not optimal to stop at $t\in[0,T)$ when $S_t-X_t$ is sufficiently large. The existence of the breakdown points $\alpha_*$ and $p_*(\alpha)$ stands in sharp contrast with the Brownian motion case (formally corresponding to $\alpha=2$), and the phenomenon itself may be attributed to the interplay between the jump structure (admitting a transition from lighter to heavier tails) and the individual preferences (represented by the error parameter $p$).