Bayesian sequential inference for nonlinear multivariate diffusions |
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Authors: | Andrew Golightly Darren J Wilkinson |
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Institution: | (1) University of Newcastle upon Tyne, NE1 7RU, UK |
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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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Keywords: | Bayesian inference Particle filter MCMC Nonlinear stochastic differential equation |
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