A computational spectral approach to interest rate models

TL;DR

This paper applies Polynomial Chaos Expansion (PCE) to interest rate models like Vasicek and CIR, providing a rigorous uncertainty quantification method with extensive numerical testing for convergence, efficiency, and accuracy compared to Monte Carlo simulations.

Contribution

It introduces PCE as a novel approach for analyzing interest rate models, demonstrating its effectiveness and reliability through comprehensive numerical experiments.

Findings

PCE accurately approximates interest rate models across various volatility scenarios.

The method shows fast convergence and high efficiency compared to Monte Carlo simulations.

PCE provides reliable estimates of distributions and quantiles for the models studied.

Abstract

The Polynomial Chaos Expansion (PCE) technique recovers a finite second order random variable exploiting suitable linear combinations of orthogonal polynomials which are functions of a given stochas- tic quantity {\xi}, hence acting as a kind of random basis. The PCE methodology has been developed as a mathematically rigorous Uncertainty Quantification (UQ) method which aims at providing reliable numerical estimates for some uncertain physical quantities defining the dynamic of certain engineering models and their related simulations. In the present paper we exploit the PCE approach to analyze some equity and interest rate models considering, without loss of generality, the one dimensional case. In particular we will take into account those models which are based on the Geometric Brownian Motion (gBm), e.g. the Vasicek model, the CIR model, etc. We also provide several numerical applications and results which are discussed for a set of volatility values. The latter allows us to test the PCE technique on a quite large set of different scenarios, hence providing a rather complete and detailed investigation on PCE-approximation's features and properties, such as the convergence of statistics, distribution and quantiles. Moreover we give results concerning both an efficiency and an accuracy study of our approach by comparing our outputs with the ones obtained adopting the Monte Carlo approach in its standard form as well as in its enhanced version.