A new approach to forecast market interest rates through the CIR model
Studies in Economics and Finance
ISSN: 1086-7376
Article publication date: 27 September 2019
Issue publication date: 23 September 2020
Abstract
Purpose
The purpose of this study is to suggest a new framework that we call the CIR#, which allows forecasting interest rates from observed financial market data even when rates are negative. In doing so, we have the objective is to maintain the market volatility structure as well as the analytical tractability of the original CIR model.
Design/methodology/approach
The novelty of the proposed methodology consists in using the CIR model to forecast the evolution of interest rates by an appropriate partitioning of the data sample and calibration. The latter is performed by replacing the standard Brownian motion process in the random term of the model with normally distributed standardized residuals of the “optimal” autoregressive integrated moving average (ARIMA) model.
Findings
The suggested model is quite powerful for the following reasons. First, the historical market data sample is partitioned into sub-groups to capture all the statistically significant changes of variance in the interest rates. An appropriate translation of market rates to positive values was included in the procedure to overcome the issue of negative/near-to-zero values. Second, this study has introduced a new way of calibrating the CIR model parameters to each sub-group partitioning the actual historical data. The standard Brownian motion process in the random part of the model is replaced with normally distributed standardized residuals of the “optimal” ARIMA model suitably chosen for each sub-group. As a result, exact CIR fitted values to the observed market data are calculated and the computational cost of the numerical procedure is considerably reduced. Third, this work shows that the CIR model is efficient and able to follow very closely the structure of market interest rates (especially for short maturities that, notoriously, are very difficult to handle) and to predict future interest rates better than the original CIR model. As a measure of goodness of fit, this study obtained high values of the statistics R2 and small values of the root of the mean square error for each sub-group and the entire data sample.
Research limitations/implications
A limitation is related to the specific dataset as we are examining the period around the 2008 financial crisis for about 5 years and by using monthly data. Future research will show the predictive power of the model by extending the dataset in terms of frequency and size.
Practical implications
Improved ability to model/forecast interest rates.
Originality/value
The original value consists in turning the CIR from modeling instantaneous spot rates to forecasting any rate of the yield curve.
Keywords
Acknowledgements
Rosa Maria Mininni and Michele Bufalo are members of Gruppo Nazionale per l’Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA) and Istituto Nazionale di Alta Matematica (INdAM). R.M. Mininni has been partially supported by the GNAMPA research project 2017 “Problemi ellittici e parabolici ed applicazioni alla Economia e alla Finanza.” Special thanks go to Roberto Renó (University of Verona – Department of Economics) for his very helpful comments.
Citation
Orlando, G., Mininni, R.M. and Bufalo, M. (2020), "A new approach to forecast market interest rates through the CIR model", Studies in Economics and Finance, Vol. 37 No. 2, pp. 267-292. https://doi.org/10.1108/SEF-03-2019-0116
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited