# Forecasting interest rates through Vasicek and CIR models: a partitioning approach

## TL;DR

This paper introduces a novel partitioning-based methodology using Vasicek and CIR models to forecast future interest rates, effectively capturing market regime changes and volatility dynamics from observed financial data.

## Contribution

It proposes a new partitioning approach that enhances interest rate forecasting with Vasicek and CIR models, addressing regime switching and volatility clustering.

## Key findings

- Method outperforms traditional models in capturing interest rate dynamics.
- Effective in low and negative interest rate environments.
- Addresses challenges like regime switching and volatility clustering.

## Abstract

The aim of this paper is to propose a new methodology that allows forecasting, through Vasicek and CIR models, of future expected interest rates (for each maturity) based on rolling windows from observed financial market data. The novelty, apart from the use of those models not for pricing but for forecasting the expected rates at a given maturity, consists in an appropriate partitioning of the data sample. This allows capturing all the statistically significant time changes in volatility of interest rates, thus giving an account of jumps in market dynamics. The performance of the new approach is carried out for different term structures and is tested for both models. It is shown how the proposed methodology overcomes both the usual challenges (e.g. simulating regime switching, volatility clustering, skewed tails, etc.) as well as the new ones added by the current market environment characterized by low to negative interest rates.