We study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances. Bid and ask returns are decomposed into a common ('efficient return') factor and two market-side-specific components...

We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of...

Bayesian learning provides the core concept of information processing in financial markets. Typically, it is assumed that market participants know perfectly the quality of released news. However, in practice, news' precision is rarely disclosed. Therefore, we extend standard Bayesian learning,...

In this paper, we provide new empirical evidence on order submission activity and price impacts of limit orders at NASDAQ. Employing NASDAQ TotalView-ITCH data, we find that market participants dominantly submit limit orders with sizes equal to a round lot. Most limit orders are canceled almost...

We analyze how markets adjust to new information when the reliability of news is uncertain and has to be estimated itself. We propose a Bayesian learning model where market participants receive fundamental information along with noisy estimates of news’ precision. It is shown that the...

Trading under limited pre-trade transparency becomes increasingly popular on financial markets. We provide first evidence on traders’ use of (completely) hidden orders which might be placed even inside of the (displayed) bid-ask spread. Employing TotalView-ITCH data on order messages at...

We quantify the short-run and long-run price effect of posting a limit order in an order book market by proposing a high-frequency cointegrated VAR model for quotes and order book depth. Estimating impulse response functions based on data from 30 stocks traded at Euronext Amsterdam we show that...

A new semiparametric proportional hazard rate model is proposed which extends standard models to include a dynamic specification. Two main problems are resolved in the course of this paper. First, the partial likelihood approach to estimate the components of a standard proportional hazard model...

We study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances. Bid and ask returns are decomposed into a common ('efficient return') factor and two market-side-specific components...

Trading under limited pre-trade transparency becomes increasingly popular on financial markets. We provide first evidence on traders' use of (completely) hidden orders which might be placed even inside of the (displayed) bid-ask spread. Employing TotalView-ITCH data on order messages at NASDAQ,...