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Full-Text Articles in Applied Statistics
Bayesian Analysis Of Structural Credit Risk Models With Microstructure Noises, Shirley J. Huang, Jun Yu
Bayesian Analysis Of Structural Credit Risk Models With Microstructure Noises, Shirley J. Huang, Jun Yu
Research Collection School Of Economics
In this paper a Markov chain Monte Carlo (MCMC) technique is developed for the Bayesian analysis of structural credit risk models with microstructure noises. The technique is based on the general Bayesian approach with posterior computations performed by Gibbs sampling. Simulations from the Markov chain, whose stationary distribution converges to the posterior distribution, enable exact ¯nite sample inferences of model parameters. The exact inferences can easily be extended to latent state variables and any nonlinear transformation of state variables and parameters, facilitating practical credit risk applications. In addition, the comparison of alternative models can be based on deviance information criterion …
Noise Reduced Realized Volatility: A Kalman Filter Approach, Douglas Steigerwald, John Owens
Noise Reduced Realized Volatility: A Kalman Filter Approach, Douglas Steigerwald, John Owens
Douglas G. Steigerwald
How should one remove microstructure noise from high-frequency asset prices? We show how to use the Kalman filter to efficiently remove microstructure noise.