Bayesian semiparametric modeling of realized covariance matrices

Flexible Bayesian Dynamic Modeling of Covariance and Correlation Matrices

Econometric analysis of vast covariance matrices using composite realized kernels∗

We propose a composite realized kernel to estimate the ex-post covariation of asset prices. Composite realized kernels are a data efficient method where the covariance estimate is composed of univariate realized kernels to estimate variances and bivariate realized kernels to estimate correlations. We analyze the merits of our composite realized kernels in an ultra high dimensional environment, ...

Semiparametric estimation of covariance matrices for longitudinal data.

Estimation of longitudinal data covariance structure poses significant challenges because the data are usually collected at irregular time points. A viable semiparametric model for covariance matrices was proposed in Fan, Huang and Li (2007) that allows one to estimate the variance function nonparametrically and to estimate the correlation function parametrically via aggregating information fro...

Semiparametric Bayesian measurement error modeling

This work introduces a Bayesian semi-parametric approach for dealing with regression models where the covariate is measured with error. The main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in the dependent and independent situations. Conditional posterior distributions are imp...

Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovations, nonparametric Bayesian methods are used to flexibly model the distribution’s skewness and kurtosis while volatility dynamics follow a parametric structure. Our Bayesian approach p...