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

Transition equity markets of Central Europe: volatility, predictability, integration

The objective of this thesis is to add evidence from the transition equity markets of Central Europe to the econometric modelling of financial time series by addressing the issues of volatility, predictability and international asset pricing in these markets. In Chapter Two we start from an overview of the transition stock markets by presenting their historical background, basic regulations, statistics, and stock market indices. Chapter Three focuses on the modelling of univariate and multivariate volatility in transition equity markets. Our 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 the models of conditional volatility. 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 correlation between three pairs of countries: Hungary and Poland, Hungary and Czech Republic, and Poland 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. Chapter Four examines the linear and nonlinear predictability of transition equity returns with simple technical trading rules. The application of the moving average trading rules to the data reveals that technical analysis helps to predict stock price changes. Firstly buy signals consistently generate higher returns than sell signals; secondly the returns following buy signals are less volatile than returns following sell signals. The application of the bootstrap methodology to check whether three popular null models of stock returns with linear conditional mean specification replicate the trading rule profits indicates that returns obtained from trading rules signals are not likely to be generated by these models. Comparison of the out-of-sample forecast performance of linear and nonlinear (feedforward networks) conditional mean estimators with past trading signals in the conditional mean equation indicates substantial forecast improvements of the feedforward network regression. Chapter Five addresses the issue of integration of the transition equity markets into the global capital market by testing pricing restrictions of the international CAPM simultaneously for four national equity markets: two developed markets (U.S. and Germany) and two new transition markets (Hungary and Poland). Methodologically, we extend the BEKK multivariate GARCH specification to accommodate GARCH-M effects, and propose an alternative specification of the conditional CAPM, which allows return volatility transmissions between the markets in the system. The results reveal that the world price of covariance risk is positive and equal across the markets. This is consistent with the international CAPM and supports the hypothesis of integration of the transition markets into the global market. However, our further results indicate individual significance of the Hungarian idiosyncratic risk, pointing to some level of segmentation of the Hungarian market. Moreover, the introduction of world-wide information variables into the system reveals that some variation in the excess national returns is still predictable after accounting for the measure of market-wide risk

Will the crisis "tear us apart"? Evidence from the EU

We examine the synchronisation of the European Union (EU) financial markets before and during the 2007 global financial crisis. We use an Asymmetric Dynamic Conditional Correlation (ADCC)-GARCH framework to control for the time-varying correlations and a Markov-Switching model to identify regime changes. Our sample considers 27 EU nations for the period 2000-2011. For each nation we formulate several characteristics of the crisis such as, synchronicity, duration and intensity measures. We find that the more recent EU members had a lagged entry to the crisis regime, were less adversely affected, show higher correlation between their stock markets and have their credit scores being revised more frequently relative to established EU members. We also find that higher levels of sovereign debt and lower levels of industrialisation positively impact crisis duration and intensity. Our results refute the notion of uniform integration of EU financial markets as evident from the highly non-synchronised observed crisis experience among the EU members. As such, one-size fits all policies are likely to be ineffective

Transition equity markets of Central Europe Volatility, predictability, integration

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN040729 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

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