Distributed ledger technologies rely on consensus protocols confronting traders with random waiting times until the transfer of ownership is accomplished. This time consuming settlement process exposes arbitrageurs to price risk and imposes limits to arbitrage. We derive theoretical arbitrage...

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...

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

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,...

An important claim of Bayesian learning and a standard assumption in price discovery models is that the strength of the price impact of unanticipated information depends on the precision of the news. In this paper, we test for this assumption by analyzing intra-day price responses of CBOT T-bond...

In this paper, we investigate the buy and sell arrival process in a limit order book market. Using an intensity framework allows to estimate the simultaneous buy and sell intensity and to derive a continuous-time measure for the buy-sell pressure in the market. Based on limit order book data...

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...

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...

In this paper, we investigate the buy and sell arrival process in a limit order book market. Using an intensity framework allows to estimate the simultaneous buy and sell intensity and to derive a continuous-time measure for the buy-sell pressure in the market. Based on limit order book data...

This paper investigates the use of price intensities to estimate volatilities based on high-frequency data. We interpret the conditional probability for the occurrence of a price event within a certain time horizon as a risk measure which allows us to obtain an estimator of the conditional...