A common practice in modeling intertrade durations is to use various hazard functions for data fitting. This paper calls into question this practice and suggests mixing the hazard function as exponential. An underlying hypothesis of the suggestion is that duration data have a mixture of...

Option is an important financial derivative. Accurate option pricing is essential to the development of financial markets. For option pricing, existing time series models and neural networks are difficult to extract multi-scale temporal features from option data, which greatly limits their...

The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t-1 particle values into time t values. In the widely-used bootstrap particle filter, this distribution is generated by the state-transition equation. While...

The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t − 1 particle values into time t values. In the widely-used bootstrap particle filter this distribution is generated by the state- transition equation. While...

We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models, wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using three examples -- an artificial state-space model, the...

In high-dimensional factor models, both the factor loadings and the number of factors may change over time. This paper proposes a shrinkage estimator that detects and disentangles these instabilities. The new method simultaneously and consistently estimates the number of pre- and post-break...