Rachidi Kotchoni

Are Cartel Fines Optimal? Theory and Evidence from the European Union

Deterring the formation or continuation of cartels is a major objective of antitrust policy. We develop a dynamic framework to characterize the compensation and deterrence properties of fines, based on the fact that cartel stability depends on the ability to prevent deviation, which itself depends in part on fines imposed in case of detection and conviction. We show that the proper consideration of cartel dynamics plays a major role in determining optimal deterrent fines. Our results suggest that fines imposed by the European Commission in recent years meet the deterrence objective in a significant number of cases.

Efficient Estimation Using the Characteristic Function

The method of moments proposed by Carrasco and Florens (2000) permits to fully exploit the information contained in the characteristic function and yields an estimator which is asymptotically as efficient as the maximum likelihood estimator. However, this estimation procedure depends on a regularization or tuning parameter a that needs to be selected. The aim of the present paper is to provide a way to optimally choose a by minimizing the approximate mean square error (AMSE) of the estimator. Following an approach similar to that of Newey and Smith (2004), we derive a higher-order expansion of the estimator from which we characterize the finite sample dependence of the AMSE on a. We provide a data-driven procedure for selecting the regularization parameter that relies on parametric bootstrap. We show that this procedure delivers a root T consistent estimator of a. Moreover, the data-driven selection of the regularization parameter preserves the consistency, asymptotic normality and efficiency of the CGMM estimator. Simulation experiments based on a CIR model show the relevance of the proposed approach.

Applications of the characteristic function-based continuum GMM in finance

A review of the theoretical properties of the GMM with a continuum of moment conditions is presented. Numerical methods for its implementation are discussed. A simulation study based on the stable distribution and an empirical application based on the autoregressive variance Gamma model are performed. Using the Alcoa price data, the findings suggest that investors require a positive premium for bearing the expected risk while a negative penalty is attached to unexpected risk.

How Much Do Cartel Overcharge

Connor and Lande (2006) conducted a survey of cartel overcharge estimates and found an average in the range of 31% to 49%. By examining more sources, Connor (2010b) finds a median of 23.3% for all type of cartels and a mean of 50.4% for successful cartels. However, the data used in these studies are estimates rather than true observations, since the true illegal profits of cartels are rarely observable. Therefore, these data are subject to model error, estimation error and publication bias. A quick glance at the Connor database reveals that the universe of overcharge estimates is asymmetric, heterogenous and contains a number of influential observations. Beside the fact that overcharge estimates are potentially biased, fitting a linear OLS model to the data without providing a careful treatment of the problems raised by the publication bias, outliers, asymmetry, and heterogeneity will necessarily produce distorted results. We conduct a meta-analysis of cartel overcharge estimates in the spirit of Connor and Bolotova (2006), but providing a sound treatment of the matters raised above. We find for cartels with initial overcharge estimates lying between 0% and 50%.a bias-corrected mean overcharge estimate of 13.6% with a median of 13.6% and for all cartels of all types a bias-corrected mean of 17.5% with a median of 14.1%.Connor and Lande (Issues in competition law and policy, pp 2203–2218, 2008 ) conducted a survey of cartels and found a mean overcharge estimate in the range of 31–49 %. By examining more sources, Connor (Price-fixing overcharges, 2nd edn. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1610262 , 2010 ) finds a mean of 50.4 % for successful cartels. However, the data that are used in those studies are estimates that are obtained from different methodologies, sources, and contexts rather than from direct observation. We conduct a meta-analysis of cartel overcharge estimates that provides a sound treatment of these matters and other data problems. We find a bias-corrected mean and median overcharge estimate of 15.47 and 16.01 %. Our results have significant antitrust policy implications. Copyright Springer Science+Business Media New York 2015

The indirect continuous-GMM estimation

A? curse of dimensionality ?arises when using the Continuum-GMM procedure to estimate large dimensional models. Two solutions are proposed, both of which convert the high dimensional model into a continuum of reduced information sets. Under certain regularity conditions, each reduced information set can be used to produce a consistent estimator of the parameter of interest. An indirect CGMM?estimator is obtained by optimally aggregating all such consistent estimators.?The simulation results suggest that the indirect CGMM procedure makes an efficient use of the information content of moment restrictions.

EFFICIENT ESTIMATION USING THE CHARACTERISTIC FUNCTION

The method of moments procedure proposed by Carrasco and Florens (2000) permits full exploitation of the information contained in the characteristic function and yields an estimator which is asymptotically as efficient as the maximum likelihood estimator. However, this estimation procedure depends on a regularization or tuning parameter I± that needs to be selected. The aim of the present paper is to provide a way to optimally choose I± by minimizing the approximate mean square error (AMSE) of the estimator. Following an approach similar to that of Donald and Newey (2001), we derive a higher-order expansion of the estimator from which we characterize the finite sample dependence of the AMSE on I±. We propose to select the regularization parameter by minimizing an estimate of the AMSE. We show that this procedure delivers a consistent estimator of I±. Moreover, the data-driven selection of the regularization parameter preserves the consistency, asymptotic normality, and efficiency of the CGMM estimator. Simulation experiments based on a CIR model show the relevance of the proposed approach.