Forecasting Interest Rates Through Vasicek and CIR Models: A Partitioning Approach

G. Orlando, R.M. Mininni and M. Bufalo"Forecasting interest rates through Vasicek and CIR models: a partitioning approach" with - Journal of Forecasting, 12 December 2019

Posted: 8 Jan 2020

See all articles by Giuseppe Orlando

Giuseppe Orlando

Università degli Studi di Bari “Aldo Moro” (UNIBA) - Department of Economics and Mathematical Methods; Università degli Studi di Camerino - School of Science and Technologies

Michele Bufalo

University of Rome I

Rosa Maria Mininni

Dipartimento di Matematica - Università degli Studi di Bari Aldo Moro

Multiple version iconThere are 2 versions of this paper

Date Written: December 12, 2019

Abstract

The aim of this paper is to propose a new methodology that allows forecasting, through Vasicek and CIR models, of future expected interest rates based on rolling windows from observed financial market data. The novelty, apart from the use of those models not for pricing but for forecasting the expected rates at a given maturity, consists in an appropriate partitioning of the data sample. This allows capturing all the statistically significant time changes in volatility of interest rates, thus giving an account of jumps in market dynamics. The performance of the new approach is carried out for different term structures and is tested for both models. It is shown how the proposed methodology overcomes both the usual challenges (e.g. simulating regime switching, volatility clustering, skewed tails, etc.) as well as the new ones added by the current market environment characterized by low to negative interest rates.

Keywords: CIR Model, Vasicek Model, Interest Rates, Forecasting and Simulation

JEL Classification: G12, E43, E47

Suggested Citation

Orlando, Giuseppe and Bufalo, Michele and Mininni, Rosa Maria, Forecasting Interest Rates Through Vasicek and CIR Models: A Partitioning Approach (December 12, 2019). G. Orlando, R.M. Mininni and M. Bufalo"Forecasting interest rates through Vasicek and CIR models: a partitioning approach" with - Journal of Forecasting, 12 December 2019. Available at SSRN: https://ssrn.com/abstract=3505673

Giuseppe Orlando (Contact Author)

Università degli Studi di Bari “Aldo Moro” (UNIBA) - Department of Economics and Mathematical Methods ( email )

Via C. Rosalba 53
VI Floor, Room 12
Bari, 70124
Italy
+39 080 5049218 (Phone)

Università degli Studi di Camerino - School of Science and Technologies ( email )

Via M. delle Carceri 9
Camerino, 62032
Italy

Michele Bufalo

University of Rome I ( email )

Rosa Maria Mininni

Dipartimento di Matematica - Università degli Studi di Bari Aldo Moro ( email )

Campus universitario - Via Orabona, 4
Bari, Bari 70125
Italy

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