IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v324y2003i1p189-195.html
   My bibliography  Save this article

Pricing derivatives by path integral and neural networks

Author

Listed:
  • Montagna, Guido
  • Morelli, Marco
  • Nicrosini, Oreste
  • Amato, Paolo
  • Farina, Marco

Abstract

Recent progress in the development of efficient computational algorithms to price financial derivatives is summarized. A first algorithm is based on a path integral approach to option pricing, while a second algorithm makes use of a neural network parameterization of option prices. The accuracy of the two methods is established from comparisons with the results of the standard procedures used in quantitative finance.

Suggested Citation

  • Montagna, Guido & Morelli, Marco & Nicrosini, Oreste & Amato, Paolo & Farina, Marco, 2003. "Pricing derivatives by path integral and neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 189-195.
  • Handle: RePEc:eee:phsmap:v:324:y:2003:i:1:p:189-195
    DOI: 10.1016/S0378-4371(02)01907-6
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437102019076
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/S0378-4371(02)01907-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Giovanni Paolinelli & Gianni Arioli, 2018. "A path integral based model for stocks and order dynamics," Papers 1803.07904, arXiv.org.
    2. Decamps, Marc & De Schepper, Ann & Goovaerts, Marc, 2006. "A path integral approach to asset-liability management," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 404-416.
    3. Paolinelli, Giovanni & Arioli, Gianni, 2018. "A path integral based model for stocks and order dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 387-399.
    4. Decamps, Marc & De Schepper, Ann & Goovaerts, Marc, 2004. "Applications of δ-function perturbation to the pricing of derivative securities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 342(3), pages 677-692.
    5. Zura Kakushadze, 2015. "Path integral and asset pricing," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1759-1771, November.
    6. Zura Kakushadze, 2014. "Path Integral and Asset Pricing," Papers 1410.1611, arXiv.org, revised Aug 2016.
    7. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    8. Fei Chen & Charles Sutcliffe, 2012. "Pricing And Hedging Short Sterling Options Using Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 128-149, April.
    9. DECAMPS, Marc & DE SCHEPPER, Ann & GOOVAERTS, Marc, "undated". "Path integrals as a tool for pricing interest rate contingent claims: The case of reflecting and absorbing boundaries," Working Papers 2003027, University of Antwerp, Faculty of Business and Economics.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. E. Ramos-P'erez & P. J. Alonso-Gonz'alez & J. J. N'u~nez-Vel'azquez, 2020. "Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network," Papers 2006.16383, arXiv.org, revised Aug 2020.
    2. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
    3. A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2022. "Deep Stochastic Optimization in Finance," Papers 2205.04604, arXiv.org.
    4. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    5. Anna Grodecka-Messi, 2019. "Subprime borrowers, securitization and the transmission of business cycles," Canadian Journal of Economics, Canadian Economics Association, vol. 52(4), pages 1600-1654, November.
    6. Dennis Bams & Thorsten Lehnert & Christian C. P. Wolff, 2009. "Loss Functions in Option Valuation: A Framework for Selection," Management Science, INFORMS, vol. 55(5), pages 853-862, May.
    7. Weiping Li & Su Chen, 2018. "The Early Exercise Premium In American Options By Using Nonparametric Regressions," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(07), pages 1-29, November.
    8. Cabrera Llanos Agustín Ignacio & Ortíz Arango Francisco, 2012. "Pronóstico del rendimiento del IPC (Índice de Precios y Cotizaciones)mediante el uso de redes neuronales diferenciales," Contaduría y Administración, Accounting and Management, vol. 57(2), pages 63-81, abril-jun.
    9. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
    10. Boero, G. & Torricelli, C., 1996. "A comparative evaluation of alternative models of the term structure of interest rates," European Journal of Operational Research, Elsevier, vol. 93(1), pages 205-223, August.
    11. Degiannakis, Stavros & Xekalaki, Evdokia, 2007. "Assessing the Performance of a Prediction Error Criterion Model Selection Algorithm in the Context of ARCH Models," MPRA Paper 96324, University Library of Munich, Germany.
    12. Haoran Wang & Xun Yu Zhou, 2020. "Continuous‐time mean–variance portfolio selection: A reinforcement learning framework," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1273-1308, October.
    13. repec:wyi:journl:002108 is not listed on IDEAS
    14. Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2021. "Accuracy of deep learning in calibrating HJM forward curves," Digital Finance, Springer, vol. 3(3), pages 209-248, December.
    15. Hsuan-Chu Lin & Ren-Raw Chen & Oded Palmon, 2016. "Explaining the volatility smile: non-parametric versus parametric option models," Review of Quantitative Finance and Accounting, Springer, vol. 46(4), pages 907-935, May.
    16. Daglish, Toby & Neely, Chris, 2008. "Optimal discrete hedging in the Heston Stochastic Volatility Model," Working Paper Series 4007, Victoria University of Wellington, The New Zealand Institute for the Study of Competition and Regulation.
    17. Purba Banerjee & Srikanth Iyer & Shashi Jain, 2023. "Multi-period static hedging of European options," Papers 2310.01104, arXiv.org, revised Oct 2023.
    18. Ait-Sahalia, Yacine & Lo, Andrew W., 2000. "Nonparametric risk management and implied risk aversion," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 9-51.
    19. M. Ryan Haley & Todd B. Walker, 2010. "Alternative tilts for nonparametric option pricing," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(10), pages 983-1006, October.
    20. Morelli, Marco J & Montagna, Guido & Nicrosini, Oreste & Treccani, Michele & Farina, Marco & Amato, Paolo, 2004. "Pricing financial derivatives with neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 160-165.
    21. Antal Ratku & Dirk Neumann, 2022. "Derivatives of feed-forward neural networks and their application in real-time market risk management," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(3), pages 947-965, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:324:y:2003:i:1:p:189-195. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.