Models for Forecasting Interest Rates with Application to Bond Portfolio Immunisation

Title: Models for Forecasting Interest Rates with Application to Bond Portfolio Immunisation Author: Kateřina Vaňková Department: Department of Probability and Mathematical Statistics Supervisor: doc. RNDr. Ing. Miloš Kopa, Ph.D., Department of Probability and Mathematical Statistics Abstract: The development and behaviour of interest rates play a crucial role in many financial fields. Interest rates can be forecasted using several models with different assumptions. In reality, these assumptions are not usually met. It leads to situations when a sophisticated and theoretically well-established model is not significantly better than simple methods, such as random walk. This thesis aims to study several approaches to interest rate forecasting, apply these approaches to European interest rate data, and find the best model for these real data. We will model European interest rates using several models. We will consider the Nelson- Siegel model (with two different approaches on how to estimate the shape parameter λ), the vector autoregression model with lag one (VAR(1)) and the Vasicek model. We will evaluate these models based on in-sample and also out-of-sample fit. We will use the Diebold-Mariano test to evaluate the statistical significance of models' forecast error differences. We select random walk as a benchmark..