The Impact of Pinning Points on Memorylessness in L\'evy Random Bridges

TL;DR

This paper investigates how the distribution of pinning points affects the memoryless property of Lévy bridges, revealing that Markovian behavior depends on whether the pinning point law has an absolutely continuous component.

Contribution

It introduces Lévy bridges with random length and pinning points and characterizes their Markov property based on the pinning point distribution.

Findings

Markov property holds if pinning point law lacks an absolutely continuous part.

Lévy bridges are non-Markovian when pinning points have an absolutely continuous distribution.

The study links the law decomposition of pinning points to the stochastic dynamics of Lévy bridges.

Abstract

Random Bridges have gained significant attention in recent years due to their potential applications in various areas, particularly in information-based asset pricing models. This paper aims to explore the potential influence of the pinning point's distribution on the memorylessness and stochastic dynamics of the bridge process. We introduce L\'evy bridges with random length and random pinning points and analyze their Markov property. Our study demonstrates that the Markov property of L\'evy bridges depends on the nature of the distribution of their pinning points. The law of any random variables can be decomposed into singular continuous, discrete, and absolutely continuous parts with respect to the Lebesgue measure (Lebesgue's decomposition theorem). We show that the Markov property holds when the pinning points' law does not have an absolutely continuous part. Conversely, the L\'evy bridge fails to exhibit Markovian behavior when the pinning point has an absolutely continuous part.