In this paper, we give an overview of the state-of-the-art in the econometric literature on the modeling of so-called financial point processes. The latter are associated with the random arrival of specific financial trading events, such as transactions, quote updates, limit orders or price...

We study the impact of the arrival of macroeconomic news on the informational andnoise-driven components in high-frequency quote processes and their conditional variances.Bid and ask returns are decomposed into a common ("ecient return") factorand two market-side-specic components capturing...

We introduce a Nelson-Siegel type interest rate term structure model with the underlying yield factors following autoregressive processes revealing time-varying stochastic volatility. The factor volatilities capture risk inherent to the term structure and are associated with the time-varying...

In this paper, we study the dynamic interdependencies between high-frequency volatility, liquidity demand as well as trading costs in an electronic limit order book market. Using data from the Australian Stock Exchange we model 1-minsquared mid-quote returns, average trade sizes, number of...

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be...

Bayesian learning provides a core concept of information processing in financialmarkets. Typically it is assumed that market participants perfectly know the qual-ity of released news. However, in practice, news’ precision is rarely disclosed. Therefore, we extend standard Bayesian learning...

We introduce a multivariate multiplicative error model which is driven by componentspecific observation driven dynamics as well as a common latent autoregressive factor. The model is designed to explicitly account for (information driven) common factor dynamics as well as idiosyncratic effects...

In this paper, we review the most common specifications of discrete-time stochasticvolatility (SV) models and illustrate the major principles of corresponding MarkovChain Monte Carlo (MCMC) based statistical inference. We provide a hands-on approachwhich is easily implemented in empirical...

This paper delineates the simultaneous impact of non-anticipated information onmean and variance of the intraday return process by including appropriate variablesaccounting for the news flow into both the mean and the variance function. This allowsus to differentiate between the consistent price...

It is well documented that the U.S. employment report has a strong price impact in financial markets. Based of these precision proxies, we show that prices respond significantly stronger to more precise information, even after controlling for an asymmetric price response to ’good’ and ’bad’...