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Forecasting interest rates through Vasicek and CIR models: A partitioning approach
Author(s) -
Orlando Giuseppe,
Mininni Rosa Maria,
Bufalo Michele
Publication year - 2020
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.2642
Subject(s) - vasicek model , interest rate , volatility (finance) , econometrics , novelty , volatility clustering , cluster analysis , computer science , economics , artificial intelligence , finance , autoregressive conditional heteroskedasticity , philosophy , theology
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 new approach is applied to 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) as well as the new ones added by the current market environment characterized by low to negative interest rates.