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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 …
Realized Daily Variance Of S&P 500 Cash Index: A Revaluation Of Stylized Facts, Shirley Huang, Qianqiu Liu, Jun Yu
Realized Daily Variance Of S&P 500 Cash Index: A Revaluation Of Stylized Facts, Shirley Huang, Qianqiu Liu, Jun Yu
Research Collection School Of Economics
In this paper the realized daily variance is obtained from intraday transaction prices of the S&P 500 cash index over the period from January 1993 to December 2004. When constructing realized daily variance, market microstructure noise is taken into account using a technique proposed by Zhang, Mykland and Ait-Sahalia (2005). The time series properties of realized daily variance are compared with those of variance estimates obtained from parametric GARCH and stochastic volatility models. Unconditional and dynamic properties concerning the realized daily variance are examined, the relationship between realized variance and returns is investigated, and the stylized facts concerning realized daily …