Finance Working Papers, Department of Business Studies, Aarhus School of Business, University of Aarhus
MCMC Based Estimation of Term Structure Models.
Abstract: We develop a state space framework for estimating term
structure models, where latent Markovian state variables are mapped
non-linearly into observable market data. The measurement equation of our
framework is explicitly constructed such that it takes raw market prices
and rates as direct inputs. We thus avoid entirely, the need for data
preprocessing, such as the use of ad hoc interpolation and data smoothing
techniques. As our general estimation approach, we demonstrate how Markov
chain Monte Carlo techniques are well suited for handling complex
functional relations between state vari-ables and data, parameter
restrictions and other features of popular term structure mod-els, which
have proved hard to handle for alternative econometric techniques. Our
estimation framework therefore handles popular multi-factor model
specifications such as exponential affine and quadratic models, but
facilitates richer Markovian HJM model specifications as well. Efficient
Markov chain Monte Carlo implementations are highly model dependent.
Therefore, having developed the general estimation principles of our
framework, we demonstrate how one could approach sampler specification for
a particular model example which we fit to a panel data set of swap and
money market rates.
Keywords: Non-linear State Space; MCMC; HJM; Factor Models; (follow links to similar papers)
39 pages, May 21, 2001
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