Approximating the first passage time density from data using generalized Laguerre polynomials

TL;DR

This paper introduces a novel method using Laguerre-Gamma polynomial approximation to estimate the first passage time density from data, applicable even with limited information, and demonstrates its effectiveness on geometric Brownian motion.

Contribution

It proposes an iterative algorithm leveraging recursion formulas for moments to approximate the first passage time density from data, including parameter estimation.

Findings

Effective density approximation from sample data.

Accurate parameter estimation using the proposed method.

Validated on geometric Brownian motion.

Abstract

This paper analyzes a method to approximate the first passage time probability density function which turns to be particularly useful if only sample data are available. The method relies on a Laguerre-Gamma polynomial approximation and iteratively looks for the best degree of the polynomial such that the fitting function is a probability density function. The proposed iterative algorithm relies on simple and new recursion formulae involving first passage time moments. These moments can be computed recursively from cumulants, if they are known. In such a case, the approximated density can be used also for the maximum likelihood estimates of the parameters of the underlying stochastic process. If cumulants are not known, suitable unbiased estimators relying on k-statistics are employed. To check the feasibility of the method both in fitting the density and in estimating the parameters, the first passage time problem of a geometric Brownian motion is considered.