reversion in the error, rather than requiring the more stringent condition of stationarity. The paper presents a Bayesian method …

regression model to achieve global identification, and presented a method for estimating this model without using the ordering … restrictions imposed in previous Bayesian and frequentist approaches. …

process. The inferential approach adopted is Bayesian, with estimation of the models proceeding via a Markov Chain Monte Carlo …

there is a controlled probability of the observed rates exceeding the stated limits is developed in this paper. A Bayesian …

A Bayesian approach to option pricing is presented, in which posterior inference about the underlying returns process …, as well as regime shifts across contract groups. The method is applied to intraday option price data on the S&P500 stock …

integrated volatility and price jumps, to the specified model components; with Bayesian inference conducted using a Markov chain … Monte Carlo algorithm. The calculation of marginal likelihoods for the proposed and related models is discussed. An … bivariate jump diffusion. A state space representation is used to link observed returns, plus nonparametric measures of …

this sampling procedure is demonstrated through Monte Carlo simulation studies. The proposed sampling algorithm is applied …

of the unknown parameters involved in the error density. A Monte Carlo simulation study shows that (i) the proposed … cross-validation. Moreover, we apply our proposed Bayesian bandwidth estimation method for the time-varying coefficient … constant estimator. In this paper, we propose a Bayesian approach to bandwidth estimation for local constant estimators of time …