Random Field and Affine Models for Interest Rates: An Empirical Comparison
نویسنده
چکیده
Traditional affine models of the term structure are eminently tractable, but suffer from empirical difficulties. Random field models offer great flexibility in fitting the data, but are widely considered non-implementable unless they are approximated by a low-dimensional system. I develop a state-space estimation framework where both random field and affine models can be estimated by MCMC using the same panel of forward rate data. I find that random field models are much better able to fit the patterns of volatility and correlation in a long historical sample of U.S. Treasury forward rates.
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