Sébastien FRIES

Grade : Doctorant contractuel GENES

Mail : sebastien.fries[arrowbase].fr

ResearchEducationJobsWork in progressOther

Research Interests

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Noncausal / forward looking / anticipative time series : conditional distribution, forecasting, statistical inference


Biography

Education

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  • Since 2015   : PhD studies in applied mathematics on forward looking, locally explosive time series, supervised by Jean-Michel Zakoian, Paris-Saclay University
  • 2012 - 2015  : Engineering degree, M.Sc., in Statistics and Probabilities at ENSAE Paristech
  • 2014 - 2015  :  M.Sc. in Quantitative Economics, Paris School of Economics
  • 2010 - 2012  : Preparatory classes for graduate schools ("Grandes Ecoles"), Fundamental Mathematics and Physics, Lycée Henri IV

 


Jobs

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  • Since 2015                      : PhD researcher at CREST
  • Summer 2015                : Research intern, French National Bank, Financial Research department, Paris, France
  • Summer 2014                : Research intern, Helmholtz Centre for Environmental Research, Computational Hydrosystems department, Leipzig, Germany
  • June 2013 - May 2014  : Data Science research intern, Deloitte, Rewards & Benefits, Neuilly-sur-Sein?e, France


Work in progress

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  • Mixed Causal/Noncausal Heavy-Tailed AR processes and the Modelling of Explosive Bubbles, with Jean-Michel Zakoian
    Abstract : Noncausal autoregressive models with heavy-tailed errors generate locally explosive processes and therefore provide a natural framework for modelling bubbles in economic and financial time series. We investigate the probability properties of mixed causal-noncausal autoregressive processes, assuming the errors follow a stable non-Gaussian distribution. We show that the tails of the conditional distribution are lighter than those of the errors, and we emphasize the presence of ARCH effects and unit roots in a causal representation of the process. Under the assumption that the errors belong to the domain of attraction of a stable distribution, we show that a weak AR causal representation of the process can be consistently estimated by classical least-squares. We derive a Monte Carlo Portmanteau test to check the validity of the weak AR representation and propose a method based on extreme residuals clustering to determine whether the AR generating process is causal, noncausal or mixed. An empirical study on simulated and real data illustrates the potential usefulness of the results.

 

  • Analytical formulae for the first four conditional moments of asymmetric alpha-stable noncausal AR(1) processes

 


Other

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Attended Conferences :

 

  • 08/2017 - 70th European Meeting of the Econometric Society, Lisbon, Portugal
  • 05/2017 - Machine Learning for Big Data, Telecom Paristech, Paris, France
  • 03/2017 - Society for Nonlinear Dynamics and Econometrics, 25th Symposium, Paris, France
  • 12/2016 - Computational and Methodological Statistics/Computational and Financial Econometrics, Sevilla, Spain
  • 07/2016 - The Society for Financial Econometrics, Summer School, Belgium
  • 02/2016 - Stochastic Processes and Statistics, CIRM Marseille, France

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