JSM 2004 - Toronto

Abstract #300717

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Activity Number: 84
Type: Contributed
Date/Time: Monday, August 9, 2004 : 8:30 AM to 10:20 AM
Sponsor: Business and Economics Statistics Section
Abstract - #300717
Title: MCMC Estimation of Multifactor Affine Term-Structure Models
Author(s): He Hu*+
Companies: University of California, Los Angeles
Address: 8130 Math Sciences Bldg., Los Angeles, CA, 90095,
Keywords: MCMC ; term-structure models
Abstract:

A number of methods have been proposed in the past years to study term-strcture models. It includes maximum likelihood and various moment-based estimations. Markov chain Monte Carlo (MCMC) methods have been applied only in recent years and have proven dramatically more efficient than other methods in high-dimensional models. Continuous time process is discretized via the Euler approximation. In order to utilize information of an arbitrary number of zero-coupon bonds, measurement errors are incorporated to the observation equation. Metropolis within Gibbs algorithm is used as the general framework. The simulation of state variable vectors greatly increases the bias of parameter estimates, which also rise with number of state variable factors. With the high computation capacity of MCMC sampler, this approach is also applicable to affine term-structure models with correlated state factors.

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