The European union

Introduction An overview of media markets shows that rapid growth and the integration of some of the most dynamic market segments are characteristics of this fast-moving industry. The main players are the established incumbents upstream and the delivery segments of media downstream. The presence of incumbents, inheritors of previous public monopolies, has led Member States to use regulation in a complementary role with competition. In these markets, strategies to deliver new products and services and to serve new geographic markets focus less on organic growth than on alliances and mergers in order to create multi-media offshoots, bid for control of content rights, increase the diffusion of products and services, and develop technologies for conditional access and transmission standards to capture advantages through proprietary technology. As a result, vertically integrated dominant positions either upstream or downstream have tended to emerge. There is nothing wrong with vertical integration except when there is market power at one stage of the vertical chain. Indeed, as far as the media industry is concerned, there are some specific challenges at the European level. The new EU regulatory framework grants some specific competition principles which can be integrated into ex ante regulation. EU merger control may also prevent potential distortion of competition resulting from the creation or the strengthening of a single or collective dominant position in the media sector at a horizontal level, or from foreclosure effects at a vertical level.

Trade Liberalization and Organizational Change

We embed a simple incomplete-contracts model of organization design in a standard two-country perfectly-competitive trade model to examine how the liberalization of product and factor markets affects the ownership structure of firms.In our model, managers decide whether or not to integrate their firms, trading off the pecuniary benefits of coordinating production decisions with the private benefits of operating in their preferred ways. The price of output is a crucial determinant of this choice, since it affects the size of the pecuniary benefits. In particular, non-integration is chosen at “low” and “high” prices, while integration occurs at moderate prices. Organizational choices also depend on the terms of trade in supplier markets, which affect the division of surplus between managers. We obtain three main results. First, even when firms do not relocate across countries, the price changes triggered by liberalization of product markets can lead to significant organizational restructuring within countries. Second, the removal of barriers to factor mobility can lead to inefficient reorganization and adversely affect consumers. Third, “deep integration” — the liberalization of both product and factor markets — leads to the convergence of organizational design across countries.

Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data

This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.