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Chair of Quantitative Finance
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Damien Challet

Damien Challet
Associate professor. Associate editor for Quantitative Finance, Applied Mathematical Finance, Journal of Economic Interaction and Coordination, Journal of Statistical Mechanics: theory and experiments.
Research interests
  • Agent behaviour (trader, investment fund, programmer). Recently, my interest has focused on agent-resolved data and aims at understanding what triggers activity and characterizing collective optimization in very heterogeneous populations.
  • Statistics: I have introduced a new way to build nonparametric statistics for location and distribution equality tests, called r-statistics. One paper is about their power and another about their efficiency as an estimator of Sharpe ratios.
  • Machine learning applied to financial data and fundamental economic data. E.g. Google Trends: see a comment and a paper.
Room: SC.113, Bouyges building damien.challet@centralesupelec.fr Tel.:  +33-(0) 1 75 31 67 55
See my CV.
Source code
There is no reason to open-source mathematical computations and not computer code when submitting a paper. My repositories may be found on github.com. Some recent contributions:
  • sharpeRratio (), an R package available on CRAN that implements the moment-free estimator of this preprint . A Python package of the same estimator, written by Amir Sani, is  available on pypi.
  • ReplacementBootstrap, an R package available on github.com repository that implements an max-entropy based bootstrap procedure, written in collaboration with Amir Sani. See this paper.
Teaching resources
Current courses
  • Physique des marchés (2012-), a mechanistic approach to financial market modelling and algorithmic trading, Applied Mathematics option for third-year students of CentraleSupélec.
  • Financial Big Data at EPFL (Master in Financial Engineering) (2016-). The whole aim is to teach how to deal with annoyingly big data, or annoyingly computationally intensive calculations (hint: clean, simplify, distribute, accelerate).
  • Algorithmic Trading (2018-), MSt in Quantitative Finance, Solvay Business School, Brussels, Belgium. In short, big data and machine learning applied to trading.
Previous courses
  • Big data, MSt in Quantitative Finance, Solvay Business School, Brussels, Belgium (2017)
  • I taught a lecture entitled “Inefficient Markets” at HEC Lausanne between 2010 and 2015. It was about why financial markets cannot be efficient. The course plan was as follows: 1) financial markets deconstruction (behavioural finance), 2) reconstruction (agent-based modelling), 3) the art of speculative strategy backtesting. Lecture slides are available there.
I will gladly supervise Master theses on a topic that is related to my interests (and, incidentally, abilities). Please check that yours are compatible with mine before contacting me.  
Press Coverage (non-exhaustive list)
  1. Tweaking taps for a constantly warm shower, New Scientist, 16 February 2008, p. 18
  2. The maths behind group showers Philip Ball, Nature (18/01/2008)
  3. Showered in surprises Michael Banks, Physics World, Volume 21 No 2, February 2008
  4. Openness makes software better sooner Nature Science update (2003)
  5. Open wide… Maths.org (2003)
  6. Le bazar et la cathédrale, ou comment l’Open Source élimine les bugs plus rapidement (2003)
  7. Forscher: Freierhältliche Quelltexte verbessern die Qualitaet von Computerprogrammen Wissenshaft.de (2003)
  8. Open Source liefert mehr Qualitaet silicon.de (2003)
  9. Debugging in OSS Always Faster slashdot.org (2003)
  10. Collective Effort Makes the Good Times Roll, Adrian Cho Science 2002 July 5; 297: 33
  11. Whole better than parts Nature Science update (2002)
  12. N/2 Wrongs Make a Right Physical Review Focus (2002)
  13. Defects combine to make perfect devices Physics Web (2002


Chair of Quantitative Finance
Laboratoire de Mathématiques et Informatique pour la Complexité et les Systèmes

9 rue Joliot-Curie
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