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Living in a Stochastic World and Managing Complex Risks

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Abstract

If there is a concept that has gained awareness during the financial crisis of 2008/2009, it is certainly the concept of risk and its consequence in risk management. The failure of many financial institutions to grasp the risks they were taking appeared so clearly and was so costly that the subject became central both with the regulators and more generally within society. Even though risk is an old concept, its perception has changed over the ages. In this century, the increase of wealth and the advances of scientific techniques may give the illusion to mankind that it has full power over Nature. People are either risk adverse or risk prone, without accepting its possible negative consequences, reactions that could be qualified as extreme and silly. Looking at it in a binary way does not help us cope with it. There is indeed little rational behavior when risk is concerned. Instead we should consider its right definition to be able to manage it. Already in the XVIII th century, philosophers came to realize that risk could contain two aspects as summarized by the French thinker Etienne Bonnot de Condillac (1714-1780) who qualified risk as " The chance of incurring a bad outcome, coupled, with the hope, if we escape it, to achieve a good one. " We see here the birth of a notion that will become prevalent in finance and economics during the XX th century.

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  • Dacorogna, Michel & Kratz, Marie, 2015. "Living in a Stochastic World and Managing Complex Risks," ESSEC Working Papers WP1517, ESSEC Research Center, ESSEC Business School.
  • Handle: RePEc:ebg:essewp:dr-15017
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    File URL: https://hal-essec.archives-ouvertes.fr/hal-01218056/document
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    1. Borscheid, Peter & Gugerli, David & Straumann, Tobias, 2013. "The Value of Risk: Swiss Re and the History of Reinsurance," OUP Catalogue, Oxford University Press, number 9780199689804 edited by James, Harold.
    2. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
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    Cited by:

    1. Nehla, Debbabi & Marie, Kratz & Mamadou , Mboup, 2016. "A self-calibrating method for heavy tailed data modeling : Application in neuroscience and finance," ESSEC Working Papers WP1619, ESSEC Research Center, ESSEC Business School.
    2. Nehla Debbabi & Marie Kratz & Mamadou Mboup, 2016. "A self-calibrating method for heavy tailed data modeling : Application in neuroscience and finance," Working Papers hal-01424298, HAL.
    3. Apicella, Giovanna & Dacorogna, Michel M, 2016. "A General framework for modelling mortality to better estimate its relationship with interest rate risks," MPRA Paper 75788, University Library of Munich, Germany.

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    More about this item

    Keywords

    extreme risk; risk management;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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