Research Seminar, ReFi – Ryan Williams – University of Arizona – CEO Mobility, Performance-Turnover Sensitivity, and Compensation: Evidence from Non-Compete Contracts.
Déc 15 @ 12 h 00 min – 13 h 30 min



Organized by Prof. Gunther Capelle-Blancard (Université Paris I Panthéon-Sorbonne, Labex ReFi) and Prof. Christophe Moussu (ESCP Europe, Labex ReFi).

Ryan Williams

University of Arizona

(cv & bio)

CEO Mobility, Performance-Turnover Sensitivity, and Compensation: Evidence from Non-Compete Contracts.

(Paper here)

Meeting Ryan Williams

Dear all,
We are having Ryan Williams (University of Arizona) next Friday in our ReFi research seminar series (see here for more information).
Ryan will be available to meet with the faculty and PhD students during the afternon. If you are interested in meeting him, please send an email to Thomas David ( indicating which of the following time slots suits you best:
14.30-15.00, 15.00-15.30, 15.30-16.00, 16.00-16.30, 16.30-17.00, 17.00-17.30


Non-compete agreements limit the mobility of employees, thereby imposing significant costs by reducing their outside options.  In this paper, we focus on CEO non-compete agreements largely because CEOs have a better ability to negotiate their employment contracts than rank-and-file employees and their joining a competitor is likely to cause the greatest economic damage to the firm. Specifically, using hand collected data on CEO non-compete agreements, we examine: (i) the determinants of CEO non-compete agreements, (ii) the impact of CEO non-compete agreements on the CEO performance-turnover sensitivity, and (iii) how CEO non-compete agreements affect the level and structure of CEO compensation. We find evidence that is consistent with the argument that the presence of a non-compete agreement is the outcome of a bargaining game between the CEO and the firm. Consistent with the idea that non-compete agreements lower the likelihood that a departing CEO can create economic harm to the firm by joining a competitor, we find that the CEO performance-turnover sensitivity is significantly stronger when the CEO has a non-compete agreement in place. Finally, we find that CEO total compensation is higher if CEOs have non-compete contracts, but that the firm pays such compensation as incentive-based pay, consistent with the ex-post realization of the ex-ante bargaining game. We exploit staggered state-level changes in non-compete enforceability to establish causality. Our paper illustrates the impact of restrictions of CEO mobility on how CEOs are monitored and compensated by the firm.


79 avenue de la République 75011 Paris
Friday  15 December 2017
12:00am  to  13:30pm, Room 4310
For security reason, please register before the deadline.
Deadline: 14 December 2017
NB. If you are prevented from coming, we would be obliged if you could inform us as soon as possible at
Workshop FinTech Series whithin the framework of the 11th International Conference on Computational and Financial Econometrics (CFE 2017)
Déc 16 Journée entière

Organized by

Dominique Guégan, University of Paris 1 (Bio & CV) and LabEx ReFi head of the FinTech research Group

Risks and fintech (CO738)

Location: Senate House, University of London

Forecasting inflection points: Hybrid methods with machine learning algorithms

by Pr. Julien Chevallier (University Paris8)

Bitcoins and challenges for financial regulation

by Pr. Dominique Guegan (University Paris1 and LabEx ReFi)

Impact of multimodality of distributions on VaR and ES calculations
by Dr. Kehan Li (Goldman Sachs)

Regulatory learning: How to supervise machine learning models with an application to credit scoring

by Dr. Bertrand Hassani (Capgemini)

Blockchain towards legal recognition in the US and EU?

by Stephane Blemus (University Paris1)


Learn more about the conference here

Thesis defence by Shohruh Miryusupov : « Particle Methods in Finance »
Déc 20 @ 16 h 00 min – 18 h 00 min

Le Labex ReFi a le plaisir de vous inviter à la soutenance de thèse de :

Shohruh Miryusupov

Doctorant à l’Université Paris 1 Panthéon-Sorbonne et au Labex ReFi

Titre de la thèse :

« Particle Methods in Finance »


Sous la direction du Professeur Raphael Douady

Le 20 décembre 2017, à 16h à la Maison des Sciences Economiques, 106 Boulevard de lHôpital Paris 13e (salle 114)

Jury de thèse


La présidente : Mme Dominique Guégan, Professeur émérite, Paris 1

M. Rama Cont, Professeur, Imperial College London, Rapporteur

M. Andrew Mullhaupt, Professeur, Stony Brook University, Rapporteur

M. Raphael Douady, Chercheur, CNRS, HDR, Centre d’Economie de la Sorbonne, Directeur de thèse

M. Pierre Del Moral, Directeur de Recherche, INRIA, Examinateur


The thesis introduces simulation techniques that are based on particle methods and it consists of two parts, namely rare event simulation and a homotopy transport for stochastic volatility model estimation.


Particle methods, that generalize hidden Markov models, are widely used in different fields such as signal processing, biology, rare events estimation, finance, etc. There are a number of approaches that are based on Monte Carlo methods that allow to approximate a target density such as Markov Chain Monte Carlo (MCMC), sequential Monte Carlo (SMC). We apply SMC algorithms to estimate default probabilities in a stable process based intensity process to compute a credit value adjustment (CVA) with a wrong way risk (WWR). We propose a novel approach to estimate rare events, which is based on the generation of Markov Chains by simulating the Hamiltonian system. We demonstrate the properties, that allows us to have ergodic Markov Chain and show the performance of our approach on the example that we encounter in option pricing.


In the second part, we aim at numerically estimating a stochastic volatility model, and consider it in the context of a transportation problem, when we would like to find « an optimal transport map » that pushes forward the measure. In a filtering context, we understand it as the transportation of particles from a prior to a posterior distribution in pseudotime. We also proposed to reweight transported particles, so as we can direct to the area, where particles with high weights are concentrated. We showed the application of our method on the example of option pricing with Stein-Stein stochastic volatility model and illustrated the bias and variance.

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