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Interest rates forecasting: Between Hull and White and the CIR#—How to make a single‐factor model work
Author(s) -
Orlando Giuseppe,
Bufalo Michele
Publication year - 2021
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2783
Subject(s) - ewma chart , econometrics , computer science , interest rate , cluster analysis , hull , cox–ingersoll–ross model , volatility (finance) , term (time) , statistics , mathematics , artificial intelligence , economics , physics , process (computing) , control chart , quantum mechanics , marine engineering , monetary economics , engineering , operating system
In this work, we present our findings of the so‐called CIR#, which is a modified version of the Cox, Ingersoll, and 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 CIR a d j ), the Hull and White model, and the exponentially weighted moving average (EWMA) which is often adopted whenever no structure in data is assumed. To confirm the analysis, testing and validation is performed on both historical and ad hoc data with different metrics and clustering criteria.