University of Arizona
(cv & bio)
CEO Mobility, Performance-Turnover Sensitivity, and Compensation: Evidence from Non-Compete Contracts.
Meeting Ryan Williams
Friday 15 December 2017
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
Le Labex ReFi a le plaisir de vous inviter à la soutenance de thèse de :
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
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.