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Elements for a theory of financial risks

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  • Jean-Philippe Bouchaud

    (Science & Finance, Capital Fund Management
    CEA Saclay;)

Abstract

Estimating and controlling large risks has become one of the main concern of financial institutions. This requires the development of adequate statistical models and theoretical tools (which go beyond the traditionnal theories based on Gaussian statistics), and their practical implementation. Here we describe three interrelated aspects of this program: we first give a brief survey of the peculiar statistical properties of the empirical price fluctuations. We then review how an option pricing theory consistent with these statistical features can be constructed, and compared with real market prices for options. We finally argue that a true `microscopic' theory of price fluctuations (rather than a statistical model) would be most valuable for risk assessment. A simple Langevin-like equation is proposed, as a possible step in this direction.

Suggested Citation

  • Jean-Philippe Bouchaud, 1998. "Elements for a theory of financial risks," Science & Finance (CFM) working paper archive 500042, Science & Finance, Capital Fund Management.
    • Handle: RePEc:sfi:sfiwpa:500042
    as

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    References listed on IDEAS

    as
    1. Rama Cont & Marc Potters & Jean-Philippe Bouchaud, 1997. "Scaling in stock market data: stable laws and beyond," Science & Finance (CFM) working paper archive 9705087, Science & Finance, Capital Fund Management.
    2. G. Caldarelli & M. Marsili & Y. -C. Zhang, 1997. "A Prototype Model of Stock Exchange," Papers cond-mat/9709118, arXiv.org.
    3. Bak, P. & Paczuski, M. & Shubik, M., 1997. "Price variations in a stock market with many agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 246(3), pages 430-453.
    4. Jean-Philippe Bouchaud & Rama Cont, 1998. "A Langevin approach to stock market fluctuations and crashes," Science & Finance (CFM) working paper archive 500027, Science & Finance, Capital Fund Management.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. A. Svorenčík & F. Slanina, 2007. "Interacting gaps model, dynamics of order book, and stock-market fluctuations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 57(4), pages 453-462, June.
    2. Jean-Philippe Bouchaud & Marc Potters, 1998. "Back to basics: historical option pricing revisited," Science & Finance (CFM) working paper archive 500036, Science & Finance, Capital Fund Management.
    3. Z. Eisler & J. Kertész, 2006. "Size matters: some stylized facts of the stock market revisited," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 51(1), pages 145-154, May.
    4. F. Slanina, 2008. "Critical comparison of several order-book models for stock-market fluctuations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 61(2), pages 225-240, January.
    5. G. Bonanno & D. Valenti & B. Spagnolo, 2006. "Role of noise in a market model with stochastic volatility," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 53(3), pages 405-409, October.
    6. M. Boguñá & J. Masoliver, 2004. "Conditional dynamics driving financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 40(3), pages 347-352, August.
    7. M. Sysi-Aho & A. Chakraborti & K. Kaski, 2003. "Intelligent minority game with genetic crossover strategies," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 34(3), pages 373-377, August.
    8. Matthias Otto, 1999. "Stochastic relaxational dynamics applied to finance: towards non-equilibrium option pricing theory," Papers cond-mat/9906196, arXiv.org, revised Oct 1999.
    9. Weron, Rafał, 2004. "Computationally intensive Value at Risk calculations," Papers 2004,32, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).

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    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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