An online estimation scheme for a Hull–White model with HMM-driven parameters |
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Authors: | Christina Erlwein Rogemar Mamon |
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Institution: | (1) CARISMA School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK;(2) Department of Statistical and Actuarial Sciences, University of Western Ontario, 2nd Floor Western Science Centre, London, ON, Canada, N6A 5B7 |
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Abstract: | This paper considers the implementation of a mean-reverting interest rate model with Markov-modulated parameters. Hidden Markov
model filtering techniques in Elliott (1994, Automatica, 30:1399–1408) and Elliott et al. (1995, Hidden Markov Models: Estimation
and Control. Springer, New York) are employed to obtain optimal estimates of the model parameters via recursive filters of
auxiliary quantities of the observation process. Algorithms are developed and implemented on a financial dataset of 30-day
Canadian Treasury bill yields. We also provide standard errors for the model parameter estimates. Our analysis shows that
within the dataset and period studied, a model with two regimes is sufficient to describe the interest rate dynamics on the
basis of very small prediction errors and the Akaike information criterion. |
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Keywords: | Regime-switching Markov model Interest rate dynamics Mean-reversion Filtering Optimal parameter estimation |
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