We study nonparametric estimation of the volatility function of a diffusion process from discrete data, when the data are blurred by additional noise. This noise can be white or correlated, and serves as a model for microstructure effects in financial modeling, when the data are given on an...

This paper introduces a solution that combines the Kalman and particle fi lters to the challenging problem of estimating integrated volatility using high-frequency data where the underlying prices are perturbed by a mixture of random noise and price discreteness. An explanation is presented of...

We present a novel method in analyzing microstructure noise of high-frequency data as a measurement error problem within an endogenous Markov-switching regression model. In this model, the regression disturbance and the latent state variable controlling the regime are correlated. We show that...

Markov models introduce persistence in the mixture distribution. In time series analysis, the mixture components relate to different persistent states characterizing the state-specific time series process. Model specification is discussed in a general form. Emphasis is put on the functional form...

Using a Markov switching unobserved component model we decompose the term premium of the North American CDX investment grade index (CDX-IG) into a permanent and a stationary component. We explain the evolution of the two components in relating them to monetary policy and stock market variables....

Using a Markov switching unobserved component model we decompose the term premium of the North American CDX investment grade index (CDX-IG) into a permanent and a stationary component. We explain the evolution of the two components in relating them to monetary policy and stock market variables....

Using a Markov switching unobserved component model we decompose the term premium of the North American CDX investment grade index (CDX-IG) into a permanent and a stationary component. We explain the evolution of the two components in relating them to monetary policy and stock market variables....

This paper examines the degree of persistence in the volatility of financial time series using a Long Memory Stochastic Volatility (LMSV) model. Specifically, it employs a Gaussian semiparametric (or local Whittle) estimator of the memory parameter, based on the frequency domain, proposed by...

This paper examines the degree of persistence in the volatility of financial time series using a Long Memory Stochastic Volatility (LMSV) model. Specifically, it employs a Gaussian semiparametric (or local Whittle) estimator of the memory parameter, based on the frequency domain, proposed by...

We propose a new time series model where the threshold is specified as an empirical quantile of recent observations of a threshold variable. The resulting conditional threshold traces the fluctuation of the threshold variable, which can enhance the fit and interpretation of the model. In the...