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A new approach to forecast market interest rates through the CIR model

Author

Listed:
  • Giuseppe Orlando
  • Rosa Maria Mininni
  • Michele Bufalo

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 statisticsR2and 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.

Suggested Citation

  • Giuseppe Orlando & Rosa Maria Mininni & Michele Bufalo, 2019. "A new approach to forecast market interest rates through the CIR model," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 37(2), pages 267-292, September.
  • Handle: RePEc:eme:sefpps:sef-03-2019-0116
    DOI: 10.1108/SEF-03-2019-0116
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    Citations

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    Cited by:

    1. Giuseppe Orlando & Rosa Maria Mininni & Michele Bufalo, 2020. "Forecasting interest rates through Vasicek and CIR models: A partitioning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 569-579, July.
    2. Bufalo, Michele & Orlando, Giuseppe, 2023. "A three-factor stochastic model for forecasting production of energy materials," Finance Research Letters, Elsevier, vol. 51(C).
    3. Marco Di Francesco & Kevin Kamm, 2021. "How to handle negative interest rates in a CIR framework," Papers 2106.03716, arXiv.org.
    4. Giuseppe Orlando & Michele Bufalo, 2021. "Interest rates forecasting: Between Hull and White and the CIR#—How to make a single‐factor model work," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1566-1580, December.
    5. Ascione, Giacomo & Mehrdoust, Farshid & Orlando, Giuseppe & Samimi, Oldouz, 2023. "Foreign Exchange Options on Heston-CIR Model Under Lévy Process Framework," Applied Mathematics and Computation, Elsevier, vol. 446(C).

    More about this item

    Keywords

    Interest rates forecasting; CIR model; Volatility clustering; ARIMA models; Numerical simulation; G12; E43; E47 2010; MSC: 91G30; 91B84; 91G60; 91G70; 62M101;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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