Abstract
In this paper, we adapt recently developed simulation-based sequential algorithms to the problem concerning the Bayesian analysis of discretely observed diffusion processes. The estimation framework involves the introduction of m−1 latent data points between every pair of observations. Sequential MCMC methods are then used to sample the posterior distribution of the latent data and the model parameters on-line. The method is applied to the estimation of parameters in a simple stochastic volatility model (SV) of the U.S. short-term interest rate. We also provide a simulation study to validate our method, using synthetic data generated by the SV model with parameters calibrated to match weekly observations of the U.S. short-term interest rate.
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Golightly, A., Wilkinson, D.J. Bayesian sequential inference for nonlinear multivariate diffusions. Stat Comput 16, 323–338 (2006). https://doi.org/10.1007/s11222-006-9392-x
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DOI: https://doi.org/10.1007/s11222-006-9392-x