Building bridges between Mathematics, Insurance and Finance

An interview with Paul Embrechts

Fabrizio Durante 1 , Giovanni Puccetti 2 ,  and Matthias Scherer 3
  • 1 Faculty of Economics & Management, Free University of Bozen/Bolzano, Italy
  • 2 Department of Economics, Management and Quantitative Methods, University of Milan, Italy
  • 3 Department of Mathematical Finance, Technische Universität München, Germany


Paul Embrechts is Professor of Mathematics at the ETH Zurich specializing in Actuarial Mathematics and Quantitative Risk Management. Previous academic positions include the Universities of Leuven, Limburg and London (Imperial College). Dr. Embrechts has held visiting professorships at several universities, including the Scuola Normale in Pisa (Cattedra Galileiana), the London School of Economics (Centennial Professor of Finance), the University of Vienna, Paris 1 (Panthéon-Sorbonne), theNationalUniversity of Singapore, KyotoUniversity,was Visiting Man Chair 2014 at the Oxford-Man Institute of Oxford University and has an Honorary Doctorate from the University of Waterloo, Heriot-Watt University, Edinburgh, and the Université Catholique de Louvain. He is an Elected Fellow of the Institute of Mathematical Statistics and the American Statistical Association, Honorary Fellow of the Institute and the Faculty of Actuaries, Actuary-SAA, Member Honoris Causa of the Belgian Institute of Actuaries and is on the editorial board of numerous scientific journals.He belongs to various national and international research and academic advisory committees. He co-authored the influential books Modelling of Extremal Events for Insurance and Finance, Springer, 1997 [8] andQuantitative RiskManagement: Concepts, Techniques and Tools, Princeton UP, 2005, 2015 [14] and published over 180 scientific papers. Dr. Embrechts consults on issues in Quantitative Risk Management for financial institutions, insurance companies and international regulatory authorities.

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  • [1] Bedford, T. and R. Cooke (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press, Cambridge.

  • [2] Breiman, L. (1965). On some limit theorems similar to the arc-sin law. Theory Probab. Appl., 10(2), 323–331.

  • [3] De Vylder, F. (1982). Best upper bounds for integrals with respect to measures allowed to vary under conical and integral constraints. Insurance Math. Econom., 1(2), 109–130.

  • [4] Donnelly, C. and P. Embrechts (2010). The devil is in the tails: actuarial mathematics and the subprime mortgage crisis. Astin Bull., 40(1), 1–33.

  • [5] Embrechts, P. (2006). Discussion of “Copulas: Tales and facts”, by Thomas Mikosch. Extremes, 9(1), 45–47.

  • [6] Embrechts, P. (2009). Copulas: a personal view. J. Risk Insurance, 76(4), 639–650.

  • [7] Embrechts, P., C. M. Goldie, and N. Veraverbeke (1979). Subexponentiality and infinite divisibility. Z. Wahrsch. verw. Gebiete, 49(3), 335–347.

  • [8] Embrechts, P., C. Klüppelberg, and T. Mikosch (1997). Modelling Extremal Events for Insurance and Finance. Springer, Berlin.

  • [9] Embrechts, P., A. J. McNeil, and D. Straumann (2002). Correlation and dependence in risk management: properties and pitfalls. In Risk Management: Value at Risk and Beyond, pp. 176–223. Cambridge University Press, Cambridge.

  • [10] Feller, W. (1971). An Introduction to Probability Theory and its Applications. Vol. II. Second edition. John Wiley & Sons, New York, NY.

  • [11] Joe, H. (1997). Multivariate Models and Dependence Concepts. Chapman & Hall, London.

  • [12] Joe, H. (2014). Dependence Modeling with Copulas. CRC Press, Boca Raton, FL.

  • [13] Lindvall, T. (1977). A probabilistic proof of Blackwell’s renewal theorem. Ann. Probability, 5(3), 482–485.

  • [14] McNeil, A. J., R. Frey, and P. Embrechts (2015). Quantitative Risk Management: Concepts, Techniques and Tools - revised edition. Princeton University Press, Princeton, NJ.

  • [15] Mikosch, T. (2006). Copulas: Tales and facts. Extremes, 9(1), 3–20.

  • [16] Nelsen, R. B. (1999). An Introduction to Copulas. Springer-Verlag, New York, NY.

  • [17] Pitman, J. W. (1974). Uniform rates of convergence for Markov chain transition probabilities. Z. Wahrsch. verw. Gebiete, 29, 193–227.

  • [18] Prohorov, Y. V. (1956). Convergence of random processes and limit theorems in probability theory. Theory Probab. Appl., 1(2), 157–214.

  • [19] Rüschendorf, L. (2013). Mathematical Risk Analysis. Dependence, Risk Bounds, Optimal Allocations and Portfolios. Springer, Heidelberg.

  • [20] Salmon, F. (2009). Recipe for disaster: the formula that killed Wall Street. Wired Magazine, 17(3).

  • [21] Salmon, F. (2012). The formula that killed Wall Street. Significance, 9(1), 16–20.

  • [22] Skorohod, A. V. (1956). Limit theorems for stochastic processes. Theory Probab. Appl., 1(3), 261–290.

  • [23] Zhang, Y. (2014). Bounded gaps between primes. Ann. of Math., 179(3), 1121–1174.


Journal + Issues

Dependence Modeling aims to provide a medium for exchanging results and ideas in the area of multivariate dependence modeling. Topics include Copula methods, environmental sciences, estimation and goodness-of-fit tests, extreme-value theory, limit laws, mass transportations, measures of association, multivariate distributions and tests, quantitative risk management, risk assessment, risk models, risk measures and stochastic orders and time series.