Interest rates mapping

TL;DR

This paper presents a geostatistical and machine learning approach to map Swiss franc interest rates across time and maturity, enabling visualization, hypothesis testing, and forecasting of interest rate curves.

Contribution

It introduces a novel combination of geostatistical models and machine learning algorithms for interest rate mapping and forecasting.

Findings

Interest rate maps facilitate pattern recognition and risk assessment.

Machine learning models effectively predict interest rate curves.

The approach supports dynamic liability simulations.

Abstract

The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and patterns perception purposes, to develop and to explore economical hypotheses, to produce dynamic asses-liability simulations and for the financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well.