In this work we present our findings of the so‐called CIR#, which is a modified version of the Cox, Ingersoll & Ross (CIR) model, turned into a forecasting tool for any term structure. The main feature of the CIR# model is its ability to cope with negative interest rates, cluster volatility and jumps. By considering a dataset composed of money market interest rates during turmoil and calmer periods, we show how the CIR# performs in terms of directionality of rates and forecasting error. Comparison is carried out with a revamped version of the CIR model (denoted CIRadj), the Hull and White model and the EWMA which is often adopted whenever no structure in data is assumed. Testing and validation is performed on both historical and had hoc data with different metrics and clustering criteria to confirm the analysis.

Interest rates forecasting: between Hull and White and the {CIR}{#}. How to make a single factor model work

Giuseppe Orlando
Conceptualization
;
2021-01-01

Abstract

In this work we present our findings of the so‐called CIR#, which is a modified version of the Cox, Ingersoll & Ross (CIR) model, turned into a forecasting tool for any term structure. The main feature of the CIR# model is its ability to cope with negative interest rates, cluster volatility and jumps. By considering a dataset composed of money market interest rates during turmoil and calmer periods, we show how the CIR# performs in terms of directionality of rates and forecasting error. Comparison is carried out with a revamped version of the CIR model (denoted CIRadj), the Hull and White model and the EWMA which is often adopted whenever no structure in data is assumed. Testing and validation is performed on both historical and had hoc data with different metrics and clustering criteria to confirm the analysis.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/368438
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