Competition between exchanges : Euronext versus Xetra

Exchanges in Europe are in a process of consolidation. After the failure of the proposed merger between Deutsche Börse and Euronext, these two groups are likely to become the nuclei for further mergers and co-operation with currently independent exchanges. A decision for one of the groups entails a decision for the respective trading platform. Against that background we evaluate the attractiveness of the two dominant continental European trading systems. Though both are anonymous electronic limit order books, there are important differences in the trading protocols. We use a matched-sample approach to compare execution costs in Euronext Paris and Xetra. We find that both quoted and effective spreads are lower in Xetra. When decomposing the spread we find no systematic differences in the adverse selection component. Realized spreads, on the other hand, are significantly higher in Euronext. Neither differences in the number of liquidity provision agreements nor differences in the minimum tick size or in the degree of domestic competition for order flow explain the different spread levels. We thus conclude that Xetra is the more efficient trading system. JEL Classification: G10, G1

Volatility in the transition markets of Central Europe

This study adds evidence from the four emerging markets of Central Europe relevant to the econometric modelling of financial time series by modelling volatility in these markets. The sample has all the previously documented characteristics of the unconditional distribution of stock returns normally used to justify the use of the GARCH class of models of conditional volatility. Both univariate and multivariate models are considered. Strong GARCH effects are apparent in all series examined. The estimates of asymmetric models of conditional volatility show rather weak evidence of asymmetries in the markets. The results of the multivariate specifications of volatility have implication for understanding the pattern of information flow between the markets. The constant correlation specification indicates significant conditional correlations between two pairs of countries: Hungary and Poland, and Hungary and Czech Republic. The BEKK model of multivariate volatility shows evidence of return volatility spillovers from Hungary to Poland, but no volatility spillover effects are found in the opposite direction.

A closer look at co-movements among stock returns

Correlation among financial assets is widely recognized; however, the mechanics of the relationship are not well understood. This paper investigates the microstructure of the co-movement of stock returns. The goal is to improve our understanding of correlation among stock returns by examining the conditions under which asset returns co-move on an intra-day basis. The methodology combines a traditional lead-lag model with a modified or pseudo-error correction model. Empirical evidence is presented to suggest the speed of adjustment between paired asset intra-day returns is a function of asymmetric information. Specifically, the wider an asset's spread, the faster the asset will converge to the intra-day returns of other similar assets. This result is consistent with partial adjustment model presented by Chan (Chan, K. (1993). Imperfect information and cross-autocorrelation among stock prices. The Journal of Finance:1211-1230.) which suggests market makers gain from monitoring other market makers in periods of uncertainty.Price dynamics Pseudo-error correction models Lead-lag models and pairs trading