IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v57y2004i10p1116-1125.html
   My bibliography  Save this article

Using neural networks for forecasting volatility of S&P 500 Index futures prices

Author

Listed:
  • Hamid, Shaikh A.
  • Iqbal, Zahid

Abstract

No abstract is available for this item.

Suggested Citation

  • Hamid, Shaikh A. & Iqbal, Zahid, 2004. "Using neural networks for forecasting volatility of S&P 500 Index futures prices," Journal of Business Research, Elsevier, vol. 57(10), pages 1116-1125, October.
  • Handle: RePEc:eee:jbrese:v:57:y:2004:i:10:p:1116-1125
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148-2963(03)00043-2
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, vol. 4(1), pages 17-46, January.
    2. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    3. Qi, Min & Zhang, Guoqiang Peter, 2001. "An investigation of model selection criteria for neural network time series forecasting," European Journal of Operational Research, Elsevier, vol. 132(3), pages 666-680, August.
    4. Qi, Min, 2001. "Predicting US recessions with leading indicators via neural network models," International Journal of Forecasting, Elsevier, vol. 17(3), pages 383-401.
    5. Barone-Adesi, Giovanni & Whaley, Robert E, 1987. "Efficient Analytic Approximation of American Option Values," Journal of Finance, American Finance Association, vol. 42(2), pages 301-320, June.
    6. Gunter Meissner & Noriko Kawano, 2001. "Capturing the volatility smile of options on high-tech stocks—A combined GARCH-neural network approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 25(3), pages 276-292, September.
    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. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    2. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 53(6), pages 286-303, January.
    3. Chiarella, Carl & Kang, Boda & Nikitopoulos, Christina Sklibosios & Tô, Thuy-Duong, 2013. "Humps in the volatility structure of the crude oil futures market: New evidence," Energy Economics, Elsevier, vol. 40(C), pages 989-1000.
    4. repec:kap:iaecre:v:14:y:2008:i:1:p:112-124 is not listed on IDEAS
    5. Yu-Shan Chen & Ke-Chiun Chang, 2009. "Using neural network to analyze the influence of the patent performance upon the market value of the US pharmaceutical companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 637-655, September.
    6. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    7. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
    8. Linying Yang & Teng Zhang & Peter Glynn & David Scheinker, 2021. "The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE)," Health Care Management Science, Springer, vol. 24(2), pages 375-401, June.
    9. Wallner, Christian & Wystup, Uwe, 2004. "Efficient computation of option price sensitivities for options of American style," CPQF Working Paper Series 1, Frankfurt School of Finance and Management, Centre for Practical Quantitative Finance (CPQF).
    10. Giandomenico, Rossano, 2006. "Valuing an American Put Option," MPRA Paper 20082, University Library of Munich, Germany.
    11. Michael J. Dueker & Thomas W. Miller, 1996. "Market microstructure effects on the direct measurement of the early exercise premium in exchange-listed options," Working Papers 1996-013, Federal Reserve Bank of St. Louis.
    12. George Chang, 2018. "Examining the Efficiency of American Put Option Pricing by Monte Carlo Methods with Variance Reduction," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(2), pages 10-13, February.
    13. Björn Lutz, 2010. "Pricing of Derivatives on Mean-Reverting Assets," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-02909-7, December.
    14. Mr. Marco Rossi, 2007. "Pricing Fund Liquidity Provision," IMF Working Papers 2007/045, International Monetary Fund.
    15. Lambrinoudakis, Costas & Skiadopoulos, George & Gkionis, Konstantinos, 2019. "Capital structure and financial flexibility: Expectations of future shocks," Journal of Banking & Finance, Elsevier, vol. 104(C), pages 1-18.
    16. Engstrom, Malin & Norden, Lars, 2000. "The early exercise premium in American put option prices," Journal of Multinational Financial Management, Elsevier, vol. 10(3-4), pages 461-479, December.
    17. Catherine Refait, 2004. "La prévision de la faillite fondée sur l’analyse financière de l’entreprise : un état des lieux," Économie et Prévision, Programme National Persée, vol. 162(1), pages 129-147.
    18. Leippold, Markus & Vasiljević, Nikola, 2017. "Pricing and disentanglement of American puts in the hyper-exponential jump-diffusion model," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 78-94.
    19. Joshua Rosenberg, 1999. "Empirical Tests of Interest Rate Model Pricing Kernels," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-015, New York University, Leonard N. Stern School of Business-.
    20. 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.
    21. Maxim Ulrich & Lukas Zimmer & Constantin Merbecks, 2023. "Implied volatility surfaces: a comprehensive analysis using half a billion option prices," Review of Derivatives Research, Springer, vol. 26(2), pages 135-169, October.

    More about this item

    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:eee:jbrese:v:57:y:2004:i:10:p:1116-1125. 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.elsevier.com/locate/jbusres .

    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.