On The Distribution Tail Of Stochastic Differential Equations: The One-Dimensional Case

TL;DR

This paper analyzes the tail behavior of solutions to certain one-dimensional SDEs, deriving the explosion point of their MGFs and providing asymptotic expansions for related distribution functions.

Contribution

It characterizes the MGF explosion for a class of SDEs and solves a nonlinear PDE to describe the distribution tail behavior, extending understanding of stochastic process distributions.

Findings

MGF explodes at a critical moment independent of drift terms

MGF expressed as sum of Cox-Ingersoll-Ross process MGF and a PDE solution

Derived sharp asymptotic expansions for the distribution tail

Abstract

This paper considers a general one-dimensional stochastic differential equation (SDE). A particular attention is given to the SDEs that may be transformed (via Ito's formula) into:$$d X\_t = ( \bar{B} (X\_t) - b X\_t) d t + \sqrt{X\_t} d W\_t, ~~~X\_0 > 0,$$where $ \bar{B}(y)/ y \to 0$. It is shown that the MGF of $X\_t$ explodes at a critical moment $\mu^\ast\_t$ which is independent of $\bar{B}$. Furthermore, this MGF is given as a sum of the MGF of a Cox-Ingersoll-Ross process plus an extra term which is given by a nonlinear partial differential equation (PDE) on $\partial\_t$ and $\partial\_x$. The existence and the uniqueness of the solution of the nonlinear PDE is then proved using the inverse function theorem in a Banach space that will be defined in the paper. As an application, the mean reverting equation $$d V\_t = ( a - b V\_t) d t + \sigma V^p\_t d W\_t, ~~~V\_0 = v\_0 > 0,$$is extensively studied where some sharp asymptotic expansions of its MGF as well as its complementary cumulative distribution (CCDF) are derived.