First Passage Time for Multivariate Jump-diffusion Stochastic Models With Applications in Finance

TL;DR

This paper introduces a Monte Carlo methodology for efficiently solving the first passage-time problem in multivariate jump-diffusion processes, with applications in finance such as default correlation and barrier options.

Contribution

The paper develops a novel Monte Carlo approach specifically tailored for multivariate jump-diffusion processes, improving efficiency over traditional methods in financial applications.

Findings

The new methodology is significantly more efficient than conventional Monte Carlo methods.

Simulation results demonstrate the method's effectiveness across various parameters.

Applicable to practical problems like default correlation and barrier option prediction.

Abstract

The ``first passage-time'' (FPT) problem is an important problem with a wide range of applications in mathematics, physics, biology and finance. Mathematically, such a problem can be reduced to estimating the probability of a (stochastic) process first to reach a critical level or threshold. While in other areas of applications the FPT problem can often be solved analytically, in finance we usually have to resort to the application of numerical procedures, in particular when we deal with jump-diffusion stochastic processes (JDP). In this paper, we develop a Monte-Carlo-based methodology for the solution of the FPT problem in the context of a multivariate jump-diffusion stochastic process. The developed methodology is tested by using different parameters, the simulation results indicate that the developed methodology is much more efficient than the conventional Monte Carlo method. It is an efficient tool for further practical applications, such as the analysis of default correlation and predicting barrier options in finance.