Convergence in probability of numerical solutions of a highly non-linear delayed stochastic interest rate model

Fuente: arXiv
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Autor principal: Coffie, Emmanuel
Formato: Preprint
Publicado: 2025
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author Coffie, Emmanuel
author_facet Coffie, Emmanuel
contents We study a delayed stochastic interest rate model with superlinearly growing coefficients and develop novel analytical tools to investigate the properties of both the true solution and its truncated Euler-Maruyama (TEM) approximation. In particular, we prove that the true solution converges in probability to the truncated EM solution as the step size approaches zero. Furthermore, we illustrate the theoretical findings through numerical experiments and validate the convergence results using an efficient Monte Carlo simulation framework for the valuation of relevant financial quantities.
format Preprint
id arxiv_https___arxiv_org_abs_2510_04092
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Convergence in probability of numerical solutions of a highly non-linear delayed stochastic interest rate model
Coffie, Emmanuel
Probability
Computational Finance
65C05, 65C30, 91G30, 91G60
We study a delayed stochastic interest rate model with superlinearly growing coefficients and develop novel analytical tools to investigate the properties of both the true solution and its truncated Euler-Maruyama (TEM) approximation. In particular, we prove that the true solution converges in probability to the truncated EM solution as the step size approaches zero. Furthermore, we illustrate the theoretical findings through numerical experiments and validate the convergence results using an efficient Monte Carlo simulation framework for the valuation of relevant financial quantities.
title Convergence in probability of numerical solutions of a highly non-linear delayed stochastic interest rate model
topic Probability
Computational Finance
65C05, 65C30, 91G30, 91G60
url https://arxiv.org/abs/2510.04092