Modelling and forecasting the covariance of financial return series has always been a challenge due to the so-called "curse of dimensionality". This paper proposes a methodology that is applicable in large dimensional cases and is based on a time series of realized covariance matrices. Some...

This dissertation consists of three stand-alone research papers, all of which treat the topic of estimation and dynamic modelling of multivariate volatility by employing the information contained in high-frequency data, which became available in the last 10 - 15 years. The main focus of all...

We analyze the relationship between spreads and an indicator for information based transactions on trade-by-trade data. Classifying trades on the NYSE in six categories with respect to their volume relative to the quoted depth, we employ an ordered probit model to predict the category of a trade...

This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter...

We propose a unified framework for estimating integrated variances and covariances based on simple OLS regressions allowing for a general market microstructure noise specification. We show that our estimators can outperform in terms of the root mean squared error criterion the most recent and...

We develop a panel intensity model, with a time varying latent factor, which captures the influence of unobserved time effects and allows for correlation across individuals. The model is designed to analyze individual trading behavior on the basis of trading activity datasets, which are...

This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter...

The expected value of sums of squared intraday returns (realized variance) gives rise to a least squares regression which adapts itself to the assumptions of the noise process and allows for a joint inference on integrated volatility (IV), noise moments and price-noise relations. In the iid...