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Estimating One-Factor Models of Short-Term Interest Rates

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  • Des Mc Manus
  • David Watt

Abstract

There currently exists in the literature several continuous-time one-factor models for short-term interest rates. This paper considers a wide range of these models that are nested into one general model. These models are approximated using both a discrete-time model and a model that accounts for aggregation effects over time, and are estimated by both the method of maximum likelihood and the general method of moments, for both Canadian and U.S. data. The estimation results are found to be independent of the approximation model used. However, the results are dependent on the estimation technique, more so for Canada than the United States. As an alternative check, the efficient method of moments is also employed. Hypothesis testing strongly suggests these one-factor models do not provide a good description of the evolution of Canadian short-term interest rates. In contrast, these models perform better for short-term U.S. interest rates.

Suggested Citation

  • Des Mc Manus & David Watt, 1999. "Estimating One-Factor Models of Short-Term Interest Rates," Staff Working Papers 99-18, Bank of Canada.
    • Handle: RePEc:bca:bocawp:99-18
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    References listed on IDEAS

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    Cited by:

    1. Diether Beuermann & Antonios Antoniou & Alejandro Bernales, 2005. "The Dynamics of the Short-Term Interest Rate in the UK," Finance 0512029, University Library of Munich, Germany.
    2. Peter Aling & Shakill Hassan, 2012. "No-Arbitrage One-Factor Models Of The South African Term Structure Of Interest Rates," South African Journal of Economics, Economic Society of South Africa, vol. 80(3), pages 301-318, September.

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    More about this item

    Keywords

    Financial markets; Interest rates;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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