Motivated by the need of a positive-semidefinite estimator of multivariate realized covariance matrices, we model noisy and asynchronous ultra-high-frequency asset prices in a state-space framework with missing data. We then estimate the covariance matrix of the latent states through a Kalman smoother and Expectation Maximization (KEM) algorithm. Iterating between the two EM steps, we obtain a ...

This paper studies properties of an estimator of mean-variance portfolio weights in a market model with multiple risky assets and a riskless asset. Theoretical formulas for the mean square error are derived in the case when asset excess returns are multivariate normally distributed and serially independent. The sensitivity of the portfolio estimator to errors arising from the estimation of the ...

The Basel II Accord requires banks to establish rigorous statistical procedures for the estimation and validation of default and ratings transition probabilities. This raises great technical challenges when sufficient default data are not available, as is the case for low default portfolios. We develop a new model that describes the typical internal credit rating process used by banks. The mode...

This paper examines jump dynamic patterns in three Chinese medical stocks. It also compares the Value-at-Risk (VaR) forecasting performance of a newly proposed realized volatility model allowing for jumps with that of two commonly used realized volatility models, which do not account for jumps. Using the Heterogeneous Autoregressive Realized Volatility model that allows for jumps (HAR-CJN), we ...

In this paper we consider the estimation of Markov models where the transition density is unknown. The approach we propose is the empirical characteristic function (ECF) estimation procedure with an approximate optimal weight function. The approximate optimal weight function is obtained through an Edgeworth/Gram-Charlier expansion of the logarithmic transition density of the Markov process. Bas...