Classification of interest rate curves using Self-Organising Maps

TL;DR

This paper applies Self-Organising Maps to classify Swiss franc interest rate curves, aiding financial risk management by identifying patterns in the term structure of interest rates.

Contribution

It introduces the use of SOM for classifying interest rate curves, providing a novel approach in financial data analysis.

Findings

Successful clustering of interest rate curves

Identification of distinct interest rate pattern groups

Enhanced understanding of term structure variations

Abstract

The present study deals with the analysis and classification of interest rate curves. Interest rate curves (IRC) are the basic financial curves in many different fields of economics and finance. They are extremely important tools in banking and financial risk management problems. Interest rates depend on time and maturity which defines term structure of the interest rate curves. IRC are composed of interest rates at different maturities (usually fixed number) which move coherently in time. In the present study machine learning algorithms, namely Self-Organising maps - SOM (Kohonen maps), are used to find clusters and to classify Swiss franc (CHF) interest rate curves.