Bayesian Estimation of CIR Model

Abstract

This article concerns the Bayesian estimation of interest rate models based on Euler-Maruyama approximation. Assume the short term interest rate follows the CIR model, an iterative method of Bayesian estimation is proposed. Markov Chain Monte Carlo simulation based on Gibbs sampler is used for the posterior estimation of the parameters. The maximum A-posteriori estimation using the genetic algorithm is employed for finding the Bayesian estimates of the parameters. The method and the algorithm are calibrated with the historical data of US Treasury bills.