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Theory of Financial Risk and Derivative Pricing

Author

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  • Bouchaud,Jean-Philippe
  • Potters,Marc

Abstract

Risk control and derivative pricing have become of major concern to financial institutions, and there is a real need for adequate statistical tools to measure and anticipate the amplitude of the potential moves of the financial markets. Summarising theoretical developments in the field, this 2003 second edition has been substantially expanded. Additional chapters now cover stochastic processes, Monte-Carlo methods, Black-Scholes theory, the theory of the yield curve, and Minority Game. There are discussions on aspects of data analysis, financial products, non-linear correlations, and herding, feedback and agent based models. This book has become a classic reference for graduate students and researchers working in econophysics and mathematical finance, and for quantitative analysts working on risk management, derivative pricing and quantitative trading strategies.

Suggested Citation

  • Bouchaud,Jean-Philippe & Potters,Marc, 2009. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521741866.
    • Handle: RePEc:cup:cbooks:9780521741866
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    Citations

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    Cited by:

    1. Peter A. Bebbington & Julius Bonart, 2016. "Order statistics of horse racing and the randomly broken stick," Papers 1612.02567, arXiv.org.
    2. Taisei Kaizoji, 2016. "Toward Economics as a New Complex System," Papers 1611.05280, arXiv.org.
    3. Zhao, Guannan & McDonald, Mark & Fenn, Dan & Williams, Stacy & Johnson, Nicholas & Johnson, Neil F., 2013. "Transition in the waiting-time distribution of price-change events in a global socioeconomic system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6458-6469.
    4. F. Baldovin & F. Camana & M. Caporin & M. Caraglio & A.L. Stella, 2015. "Ensemble properties of high-frequency data and intraday trading rules," Quantitative Finance, Taylor & Francis Journals, vol. 15(2), pages 231-245, February.
    5. Ilaria Bordino & Stefano Battiston & Guido Caldarelli & Matthieu Cristelli & Antti Ukkonen & Ingmar Weber, 2012. "Web Search Queries Can Predict Stock Market Volumes," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-17, July.
    6. repec:dau:papers:123456789/7471 is not listed on IDEAS
    7. G., Mauricio Contreras & Peña, Juan Pablo, 2019. "The quantum dark side of the optimal control theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 450-473.
    8. C. M. Rodr'iguez-Mart'inez & H. F. Coronel-Brizio & A. R. Hern'andez-Montoya, 2019. "A multi-scale symmetry analysis of uninterrupted trends returns of daily financial indices," Papers 1908.11204, arXiv.org.
    9. Eliazar, Iddo, 2014. "From entropy-maximization to equality-maximization: Gauss, Laplace, Pareto, and Subbotin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 479-492.
    10. Charles-Albert Lehalle & Guillaume Simon, 2021. "Portfolio selection with active strategies: how long only constraints shape convictions," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 443-463, October.
    11. David Marcos, 2020. "Transaction Costs in Execution Trading," Papers 2007.07998, arXiv.org.
    12. Il Gu Yi & Gabjin Oh & Beom Jun Kim, 2013. "Fractality of profit landscapes and validation of time series models for stock prices," Papers 1308.1749, arXiv.org.
    13. Rodríguez-Martínez, C.M. & Coronel-Brizio, H.F. & Hernández-Montoya, A.R., 2021. "A multi-scale symmetry analysis of uninterrupted trends returns in daily financial indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    14. Mitsuaki Murota & Jun-ichi Inoue, 2014. "Large-scale empirical study on pairs trading for all possible pairs of stocks listed on the first section of the Tokyo Stock Exchange," Papers 1412.7269, arXiv.org, revised Mar 2015.
    15. Fang, Ming & Taylor, Stephen & Uddin, Ajim, 2022. "The network structure of overnight index swap rates," Finance Research Letters, Elsevier, vol. 46(PB).
    16. Trojan, Sebastian, 2013. "Regime Switching Stochastic Volatility with Skew, Fat Tails and Leverage using Returns and Realized Volatility Contemporaneously," Economics Working Paper Series 1341, University of St. Gallen, School of Economics and Political Science, revised Aug 2014.
    17. Ma, Chao & Ma, Qinghua & Yao, Haixiang & Hou, Tiancheng, 2018. "An accurate European option pricing model under Fractional Stable Process based on Feynman Path Integral," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 87-117.
    18. Schinckus, C., 2013. "Between complexity of modelling and modelling of complexity: An essay on econophysics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3654-3665.
    19. Christopher M Wray & Steven R Bishop, 2016. "A Financial Market Model Incorporating Herd Behaviour," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-28, March.
    20. Thilo A. Schmitt & Rudi Schäfer & Dominik Wied & Thomas Guhr, 2016. "Spatial dependence in stock returns: local normalization and VaR forecasts," Empirical Economics, Springer, vol. 50(3), pages 1091-1109, May.
    21. Eliazar, Iddo & Cohen, Morrel H., 2011. "The universal macroscopic statistics and phase transitions of rank distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4293-4303.
    22. Anshul Verma & Riccardo Junior Buonocore & Tiziana di Matteo, 2017. "A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering," Papers 1712.02138, arXiv.org, revised May 2018.

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