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Maximum likelihood estimation of the Cox-Ingersoll-Ross model using particle filters

Author

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  • Giuliano De Rossi

Abstract

I show that the QML procedure, used in many papers in the current literature to estimate the CIR model from time series data, is based on an approximation of the latent factors' density that becomes very inaccurate for typical parameter values. I also argue that this issue is not addressed by the Monte Carlo experiments carried out to support the conclusion that the QML bias is negligible. The second part of the paper describes a computationally efficient maximum likelihood estimator based on particle filters. The advantage of this estimator is that it takes into account the exact likelihood function while avoiding the huge computational burden associated with MCMC methods. The proposed methodology is implemented and tested on a sample of simulated data

Suggested Citation

  • Giuliano De Rossi, 2004. "Maximum likelihood estimation of the Cox-Ingersoll-Ross model using particle filters," Computing in Economics and Finance 2004 302, Society for Computational Economics.
    • Handle: RePEc:sce:scecf4:302
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    References listed on IDEAS

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    Cited by:

    1. Andreasen, Martin M & Meldrum, Andrew, 2015. "Market beliefs about the UK monetary policy life-off horizon: a no-arbitrage shadow rate term structure model approach," Bank of England working papers 541, Bank of England.
    2. Martin Andreasen, 2010. "How to Maximize the Likelihood Function for a DSGE Model," Computational Economics, Springer;Society for Computational Economics, vol. 35(2), pages 127-154, February.

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    More about this item

    Keywords

    Particle filtering; Term structure of interest rates;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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