2007 : "Agregation du superieur en Sciences de Gestion"
2006 :  Accreditation to supervise research, University of Lyon
2002 : Ph.D. in Sciences, university of Nice Sophia-Antipolis, France
1996-2000 : M.S. and Agregation in physics, Ecole Normale Supérieure de Lyon, France

Current positions:
Professor of Finance, University of Saint-Etienne, Institute of Business Administration (ISEAG-IAE), France, since Sept. 2007
Part-time Associate Professor of Finance, EM Lyon Business School, Dept. Economic, Finance & Control, France, since Sept. 2004

Past positions:
Sept 2006 - Aug. 2007: Senior Researcher,  ETH Zürich, Chair of Entrepreneurial Risks, Switzerland
Sept 2003 - Aug. 2006: Assistant Professor of Finance, University of Lyon, ISFA Graduate School of Actuarial Science, France
Sept 2000 - Aug. 2003: Research and teaching assistant, University of Nice Sophia-Antipolis, France

Book
MALEVERGNE, Yannick & SORNETTE, Didier. 2005. Extreme Financial Risks : From Dependence to Risk Management  (Springer) – 311 p., 62 illus., ISBN: 3-540-27264-X.

Book reviews
Y. Malevergne (2005): “Preparing for the Worst: Incorporating Downside Risk in Stock Market” by H.D. Vinod et D.P. Reagle (Wiley), Journal of the American Statistical Association 100(472), 1459-1460.
Y. Malevergne (2004): “Why Stock Market Crash?” by D. Sornette (Princeton University Press), Finance 25(2), 49-52.

Publications in peered reviews
Y. Malevergne, V. Pisarenko et D. Sornette, (2006): “The modified Weibull distribution for asset returns: reply”, Quantitative Finance 6, 451.
A. Chabaane, J.P. Laurent, Y. Malevergne et F. Turpin, (2006): “Alternative risk measures for alternative investments”, Journal of Risk 8(4), 1-32.  Repinted in: The Value-at-Risk Reference: Key Issues in the Implementation of Market Risk, J. Danielsson, ed. (RiskBooks).
Y. Malevergne, V. Pisarenko et D. Sornette, (2006): “On the power of generalized extreme value (GEV) and generalized Pareto distribution (GPD) estimators for empirical distributions of log-returns”, Applied Financial Economics 16(3), 271-289.
Y. Malevergne, V. Pisarenko et D. Sornette, (2005): “Empirical distributions of stock returns: Exponential or power-like?”, Quantitative Finance 5, 379-401.
Y. Malevergne et D. Sornette, (2005): “Higher-moment portfolio theory: Capitalizing on behavioral anomalies of stock markets”, Journal of Portfolio Management 31(4), 49-55.
Y. Malevergne et D. Sornette, (2005): “High-order moments and cumulants of multivariate Weibull asset returns distributions: Analytical theory and empirical tests – II”, Finance Letters 3(1), special issue on “Modelling of the equity market”, F.J. Fabozzi, S.M. Focardi and P.N. Kolm, eds., 54-63.
Y. Malevergne et D. Sornette, (2004): “Multivariate Weibull distributions for asset returns – I”, Finance Letters 2(6), 16-32.
Y. Malevergne et D. Sornette, (2004): “How to account for extreme co-movements between individual stocks and the market”, Journal of Risk 6(3), 71-116. Reprinted: The Value-at-Risk Reference: Key Issues in the Implementation of Market Risk, J. Danielsson, ed. (RiskBooks).
Y. Malevergne et D. Sornette (2004): “VaR-Efficient portfolios for a class of super and sub-exponentially decaying assets return distributions”, Quantitative Finance 4, 17-36.
D. Sornette, Y. Malevergne et J.F. Muzy (2003): “What causes crashes?”, Risk 16(2), 67-71.
Y. Malevergne et D. Sornette (2003): “Testing the Gaussian copula hypothesis for financial assets dependence”, Quantitative Finance 3, 231-250.
Y. Malevergne et D. Sornette (2002): “Minimizing extremes”, Risk 15(11), 129-132.
A. Corcos, J.-P. Eckmann, A. Malaspinas, Y. Malevergne et D. Sornette (2002): “Imitation and contrarian behavior: hyperbolic bubbles, crashes and chaos”, Quantitative Finance 2, 264-281. Reprinted in: International Finance from Macroeconomics to Econophysics, S. Da Silva, ed. (Nova Science), chapitre 17.
Y. Malevergne et D. Sornette (2001): “Multi-dimensional rational bubbles and fat tails”, Quantitative Finance 1, 533-541.

Chapters in books
Y. Malevergne et D. Sornette (2006): “Multi-moments methods for portfolio management: Generalized asset pricing model in homogenenous and heterogeneous markets”, in B. Maillet et E. Jurczenko (eds.): Multi-moment Asset Allocation and Pricing Models (Wiley & Sons), pp. 165-193.
D. Sornette, Y. Malevergne et J.F. Muzy (2004): “Volatility fingerprints of large shocks: Endogenous versus exogenous”, in H. Takayasu (ed.): Application of Econophysics, Proceedings of the second Nikkei symposium on econophysics (Springer Verlag).

Other Publications
Y.Malevergne and D. Sornette (2007): "Self-consistent asset pricing models", Physica A 382, 149-171.
J.V. Andersen, Y. Malevergne and D. Sornette (2002): “Comprendre et gérer les risques grands et extrêmes”, Revue Risques – Les Cahiers de l’Assurance 49, 105-110
D. Sornette and Y. Malevergne (2001): "From rational bubbles to crashes", Physica A 299, 40-59.

Marqui's Who's Who in the World (2007)
Research grant on Extreme Financial Risks, University of Lyon, 2004
French Ministry of Education grant for doctoral studies (2000-2003)

French
English

Research Interests:
Financial risks management, Portfolio management, Alternative investments, Empirical Finance…

Refereeing Activity
Referee for Finance, Frontiers in Finance and Economics, International Journal of Theoretical and Applied Finance, Journal of the American Statistical Association, Journal of Statistical Planning and Inference, Quantitative Finance, Review of Economics and Statistics, Risk Magazine,…

Work

• Extreme Financial Risks : From Dependance to Risk Management
  Yannick Malevergne et Didier Sornette
  Portfolio analysis and optimization, together with the associated risk assessment and management, require knowledge of the likely distributions of returns at different time scales and insights into the nature and properties of dependences between the different assets. This innovative treatment offers an original and thorough treatment of these two domains, focusing mainly on the concepts and tools that remain valid for large and extreme price moves. Strong emphasis is placed on the theory of copulas and their empirical testing and calibration, because they offer intrinsic and complete measures of dependences.
  Springer Editions