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Quantitative Finance and Economics

2019, Volume 3, Issue 3: 490-507. doi: 10.3934/QFE.2019.3.490
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Quantum option pricing and data analysis

  • 1.
    University of Leicester, Department of Mathematics, University Road, Leicester LE1 7RH, United Kingdom
  • 2.
    Université Libre de Bruxelles, Département de Mathématique, Campus Plaine C.P. 210, B-1050 Bruxelles, Belgium
  • Received: 24 May 2019 Accepted: 17 July 2019 Published: 01 August 2019

  • JEL Codes: C02, C10, C53, G12

  • The paper proposes to treat financial models using techniques of quantum mechanics. The methodology relies on the Dirac matrix formalism and the Feynman path integral approach. This leads us to reexamine in this framework the classical option pricing models of Cox-Ross-Rubinstein and Black-Scholes. Moreover, financial data are classified with respect to the spectrum of a certain observable and then analyzed to identify price jumps using supervised machine learning tools.

    Citation: Wenyan Hao, Claude Lefèvre, Muhsin Tamturk, Sergey Utev. Quantum option pricing and data analysis[J]. Quantitative Finance and Economics, 2019, 3(3): 490-507. doi: 10.3934/QFE.2019.3.490

    Related Papers:

  • The paper proposes to treat financial models using techniques of quantum mechanics. The methodology relies on the Dirac matrix formalism and the Feynman path integral approach. This leads us to reexamine in this framework the classical option pricing models of Cox-Ross-Rubinstein and Black-Scholes. Moreover, financial data are classified with respect to the spectrum of a certain observable and then analyzed to identify price jumps using supervised machine learning tools.


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