IDEAS home Printed from https://ideas.repec.org/p/wpa/wuwpma/0501012.html
   My bibliography  Save this paper

Neural Networks as tools for increasing the forecast and control of complex economic systems. Economics & Complexity - 1999\Vol2 N2 Spec. NEU 99-a

Author

Listed:
  • Salzano Massimo

    (Università di Salerno Dipartimento di Scienze Economiche e Statistiche)

Abstract

The idea that NN can be usefully used for a better understanding of economic complex mechanisms is present in the literature. Our interest is to show that this is correct if we use the larger possible amounts of information that data conveys. At this end we will start with the consideration expressed by Mandelbrot that a traditional model could explain the economic behaviour 95% of time, but that in terms of amount the remaining 5% means quite the complete set of phenomena that we want to understand. We need complex models for dealing with this part. For their characteristic of being general approximators NNs seem one of most interesting instrument. This is true both for macroeconomic and for financial data.Often, the economic system is so complex that, to grasp the meaning of the information conveyed by the data, even a general approximator like NN is not enough. Larger information could be obtained using 2 or more instruments in cascade or in parallel. We will concentrate on this topic. We will try to illustrate how the combination of tools is possible. Applications will refer to Italian macroeconomic and financial data.

Suggested Citation

  • Salzano Massimo, 2005. "Neural Networks as tools for increasing the forecast and control of complex economic systems. Economics & Complexity - 1999\Vol2 N2 Spec. NEU 99-a," Macroeconomics 0501012, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpma:0501012
    Note: Type of Document - pdf; pages: 10
    as

    Download full text from publisher

    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/mac/papers/0501/0501012.pdf
    Download Restriction: no
    ---><---

    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.
    2. Haefke, Christian & Helmenstein, Christian, 1996. "Neural Networks in the Capital Markets: An Application to Index Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 9(1), pages 37-50, February.
    Full references (including those not matched with items on IDEAS)

    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. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    2. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, January.
    3. Michaelides, Panayotis G. & Vouldis, Angelos T. & Tsionas, Efthymios G., 2010. "Globally flexible functional forms: The neural distance function," European Journal of Operational Research, Elsevier, vol. 206(2), pages 456-469, October.
    4. Lam, K. & Chang, E. & Lee, M. C., 2002. "An empirical test of the variance gamma option pricing model," Pacific-Basin Finance Journal, Elsevier, vol. 10(3), pages 267-285, June.
    5. 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.
    6. 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.
    7. Patrick Büchel & Michael Kratochwil & Maximilian Nagl & Daniel Rösch, 2022. "Deep calibration of financial models: turning theory into practice," Review of Derivatives Research, Springer, vol. 25(2), pages 109-136, July.
    8. 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.
    9. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    10. Engelbert Stockhammer & Paul Ramskogler, 2008. "Uncertainty and Exploitation in History," Journal of Economic Issues, Taylor & Francis Journals, vol. 42(1), pages 175-194, March.
    11. Harald Badinger & Ingrid Kubin, 2007. "Vom kurzfristigen zum mittelfristigen Gleichgewicht in einer offenen Volkswirtschaft unter fixen und flexiblen Wechselkursen," Department of Economics Working Papers wuwp101, Vienna University of Economics and Business, Department of Economics.
    12. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    13. Jozef Baruník, 2008. "How Do Neural Networks Enhance the Predictability of Central European Stock Returns?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 58(07-08), pages 358-376, Oktober.
    14. A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2022. "Deep Stochastic Optimization in Finance," Papers 2205.04604, arXiv.org.
    15. Jaydip Sen & Tamal Datta Chaudhuri, 2017. "A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector," Papers 1705.01144, arXiv.org.
    16. 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.
    17. Philippe Paquet, 1997. "L'utilisation des réseaux de neurones artificiels en finance," Working Papers 1997-1, Laboratoire Orléanais de Gestion - université d'Orléans.
    18. Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019. "Pricing Options and Computing Implied Volatilities using Neural Networks," Risks, MDPI, vol. 7(1), pages 1-22, February.
    19. 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.
    20. Wolff, Christian & Bams, Dennis & Lehnert, Thorsten, 2005. "Loss Functions in Option Valuation: A Framework for Model Selection," CEPR Discussion Papers 4960, C.E.P.R. Discussion Papers.

    More about this item

    Keywords

    Neural Network; Public Finance; Control of Economics; Macroeconomics;
    All these keywords.

    JEL classification:

    • E - Macroeconomics and Monetary Economics

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:wpa:wuwpma:0501012. 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: EconWPA (email available below). General contact details of provider: https://econwpa.ub.uni-muenchen.de .

    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.