# Estimating an affine term structure model of interest rates with correlated noise

## TL;DR

This paper improves the estimation of interest rate models by addressing the issue of correlated noise in Kalman filtering.

## Contribution

A new measurement expansion scheme is proposed to handle correlated noise in affine term structure models.

## Key findings

- The measurement expansion scheme improves estimation accuracy under correlated noise.
- Simulation results show better performance compared to traditional methods.
- The approach leverages the whitening properties of the Kalman filter.

## Abstract

Kalman filtering for the affine term structure model of interest rates is typically applied under the assumption of white noise. However, correlated noise frequently occurs during actual data processing. The accuracy and reliability of the filter are compromised if the correlated noise is assumed to be white noise. This paper develops a measurement expansion scheme for the affine term structure model based on the whitening properties of the Kalman filter, enabling latent factor estimation under the general assumption of correlated noise. The simulation results indicate that the estimation based on the measurement expansion scheme achieves higher accuracy compared to the traditional method.