IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v387y2008i13p3192-3200.html
   My bibliography  Save this article

Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices

Author

Listed:
  • Tseng, Chih-Hsiung
  • Cheng, Sheng-Tzong
  • Wang, Yi-Hsien
  • Peng, Jin-Tang

Abstract

This investigation integrates a novel hybrid asymmetric volatility approach into an Artificial Neural Networks option-pricing model to upgrade the forecasting ability of the price of derivative securities. The use of the new hybrid asymmetric volatility method can simultaneously decrease the stochastic and nonlinearity of the error term sequence, and capture the asymmetric volatility. Therefore, analytical results of the ANNS option-pricing model reveal that Grey-EGARCH volatility provides greater predictability than other volatility approaches.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:13:p:3192-3200
    DOI: 10.1016/j.physa.2008.01.074
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437108000320
    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/j.physa.2008.01.074?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. Burak Saltoglu, 2003. "Comparing forecasting ability of parametric and non-parametric methods: an application with Canadian monthly interest rates," Applied Financial Economics, Taylor & Francis Journals, vol. 13(3), pages 169-176.
    2. 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.
    3. Harvey, Campbell R. & Siddique, Akhtar, 1999. "Autoregressive Conditional Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(4), pages 465-487, December.
    4. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    5. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Kanas, Angelos & Yannopoulos, Andreas, 2001. "Comparing linear and nonlinear forecasts for stock returns," International Review of Economics & Finance, Elsevier, vol. 10(4), pages 383-398, December.
    8. Kanas, Angelos, 2001. "Neural Network Linear Forecasts for Stock Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(3), pages 245-254, July.
    9. Duan, Jin-Chuan & Zhang, Hua, 2001. "Pricing Hang Seng Index options around the Asian financial crisis - A GARCH approach," Journal of Banking & Finance, Elsevier, vol. 25(11), pages 1989-2014, November.
    10. Yao, Jingtao & Li, Yili & Tan, Chew Lim, 2000. "Option price forecasting using neural networks," Omega, Elsevier, vol. 28(4), pages 455-466, August.
    11. Ang, Andrew & Chen, Joseph, 2002. "Asymmetric correlations of equity portfolios," Journal of Financial Economics, Elsevier, vol. 63(3), pages 443-494, March.
    12. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    13. G P Zhang & V L Berardi, 2001. "Time series forecasting with neural network ensembles: an application for exchange rate prediction," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(6), pages 652-664, June.
    14. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    15. Teo Jasic & Douglas Wood, 2004. "The profitability of daily stock market indices trades based on neural network predictions: case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965-1999," Applied Financial Economics, Taylor & Francis Journals, vol. 14(4), pages 285-297.
    16. Malhotra, Rashmi & Malhotra, D. K., 2003. "Evaluating consumer loans using neural networks," Omega, Elsevier, vol. 31(2), pages 83-96, April.
    17. Mark E. Wohar & David E. Rapach, 2005. "Valuation ratios and long-horizon stock price predictability," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 327-344.
    18. Campbell R. Harvey & Akhtar Siddique, 2000. "Conditional Skewness in Asset Pricing Tests," Journal of Finance, American Finance Association, vol. 55(3), pages 1263-1295, June.
    19. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
    20. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    21. Henrik Amilon, 2003. "A neural network versus Black-Scholes: a comparison of pricing and hedging performances," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 317-335.
    22. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    23. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    24. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    25. Angela J. Black & David G. McMillan, 2004. "Non‐linear Predictability of Value and Growth Stocks and Economic Activity," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(3‐4), pages 439-474, April.
    26. Heston, Steven L & Nandi, Saikat, 2000. "A Closed-Form GARCH Option Valuation Model," Review of Financial Studies, Society for Financial Studies, vol. 13(3), pages 585-625.
    27. Qi, Min & Wu, Yangru, 2003. "Nonlinear prediction of exchange rates with monetary fundamentals," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 623-640, December.
    28. 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.
    29. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.
    30. Qi, Min, 1999. "Nonlinear Predictability of Stock Returns Using Financial and Economic Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 419-429, October.
    31. Dunis, Christian L & Huang, Xuehuan, 2002. "Forecasting and Trading Currency Volatility: An Application of Recurrent Neural Regression and Model Combination," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(5), pages 317-354, August.
    32. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2005. "A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 665-680.
    33. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    34. Jin‐Chuan Duan, 1995. "The Garch Option Pricing Model," Mathematical Finance, Wiley Blackwell, vol. 5(1), pages 13-32, January.
    35. Joseph Plasmans & William Verkooijen & Hennie Daniels, 1998. "Estimating structural exchange rate models by artificial neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 541-551.
    36. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
    37. Alan E. H. Speight & David G. McMillan, 2004. "Daily volatility forecasts: reassessing the performance of GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 449-460.
    38. Shively, Philip A., 2003. "The nonlinear dynamics of stock prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 43(3), pages 505-517.
    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. Tapia, Sebastian & Kristjanpoller, Werner, 2022. "Framework based on multiplicative error and residual analysis to forecast bitcoin intraday-volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    2. Dimitrios Kartsonakis Mademlis & Nikolaos Dritsakis, 2021. "Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 11(1), pages 49-60.
    3. Rostamian, Hossein & Lotfollahi, Mohammad Nader, 2020. "Statistical modeling of aspirin solubility in organic solvents by Response Surface Methodology and Artificial Neural Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    4. Radosław Puka & Bartosz Łamasz, 2020. "Using Artificial Neural Networks to Find Buy Signals for WTI Crude Oil Call Options," Energies, MDPI, vol. 13(17), pages 1-20, August.
    5. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    6. 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.
    7. Adam Fadlalla & Farzaneh Amani, 2014. "Predicting Next Trading Day Closing Price Of Qatar Exchange Index Using Technical Indicators And Artificial Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(4), pages 209-223, October.
    8. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2014. "An Evolving Fuzzy-Garch Approach Forfinancial Volatility Modeling And Forecasting," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 138, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    9. Shih, Kuang Hsun & Cheng, Ching Chan & Wang, Yi Hsien, 2011. "Financial Information Fraud Risk Warning for Manufacturing Industry - Using Logistic Regression and Neural Network," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 54-71, March.
    10. Maya Malinda & Jo-Hui Chen, 2022. "The forecasting of consumer exchange-traded funds (ETFs) via grey relational analysis (GRA) and artificial neural network (ANN)," Empirical Economics, Springer, vol. 62(2), pages 779-823, February.
    11. Lukas Ryll & Sebastian Seidens, 2019. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Papers 1906.07786, arXiv.org, revised Jul 2019.
    12. Lahmiri, Salim, 2017. "Modeling and predicting historical volatility in exchange rate markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 387-395.
    13. Manel Hamdi & Walid Chkili, 2019. "An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter?," Working Papers 13, Economic Research Forum, revised 21 Aug 2019.
    14. Leandro Maciel, 2012. "A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting," Brazilian Review of Finance, Brazilian Society of Finance, vol. 10(3), pages 337-367.
    15. Lahmiri, Salim, 2016. "Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 388-396.
    16. Zhang, Lihong & Wang, Jun & Wang, Bin, 2020. "Energy market prediction with novel long short-term memory network: Case study of energy futures index volatility," Energy, Elsevier, vol. 211(C).
    17. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2016. "Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 379-398, October.
    18. Chih-Chung Yang & Yungho Leu & Chien-Pang Lee, 2014. "A Dynamic Weighted Distancedbased Fuzzy Time Series Neural Network with Bootstrap Model for Option Price Forecasting," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 115-129, June.

    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. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    2. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    3. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    4. Lin, Shin-Hung & Huang, Hung-Hsi & Li, Sheng-Han, 2015. "Option pricing under truncated Gram–Charlier expansion," The North American Journal of Economics and Finance, Elsevier, vol. 32(C), pages 77-97.
    5. Stentoft, Lars, 2005. "Pricing American options when the underlying asset follows GARCH processes," Journal of Empirical Finance, Elsevier, vol. 12(4), pages 576-611, September.
    6. Font, Begoña, 1998. "Modelización de series temporales financieras. Una recopilación," DES - Documentos de Trabajo. Estadística y Econometría. DS 3664, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Yi-Hsien Wang, 2009. "Using neural network to forecast stock index option price: a new hybrid GARCH approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 43(5), pages 833-843, September.
    8. Lars Stentoft, 2008. "American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 540-582, Fall.
    9. Zhu, Ke & Ling, Shiqing, 2015. "Model-based pricing for financial derivatives," Journal of Econometrics, Elsevier, vol. 187(2), pages 447-457.
    10. Stentoft, Lars, 2011. "American option pricing with discrete and continuous time models: An empirical comparison," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 880-902.
    11. Bronka Rzepkowski, 2001. "Pouvoir prédictif de la volatilité implicite dans le prix des options de change," Économie et Prévision, Programme National Persée, vol. 148(2), pages 71-97.
    12. Peter A. Abken & Saikat Nandi, 1996. "Options and volatility," Economic Review, Federal Reserve Bank of Atlanta, vol. 81(Dec), pages 21-35.
    13. Peter Christoffersen & Kris Jacobs, 2004. "Which GARCH Model for Option Valuation?," Management Science, INFORMS, vol. 50(9), pages 1204-1221, September.
    14. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    15. 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.
    16. Wu, Guojun & Xiao, Zhijie, 2002. "A generalized partially linear model of asymmetric volatility," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 287-319, August.
    17. Peter Christoffersen & Kris Jacobs, 2002. "Which Volatility Model for Option Valuation?," CIRANO Working Papers 2002s-33, CIRANO.
    18. repec:dau:papers:123456789/2138 is not listed on IDEAS
    19. Vipul Kumar Singh, 2013. "Effectiveness of volatility models in option pricing: evidence from recent financial upheavals," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 10(3), pages 352-375, October.
    20. Badescu Alex & Kulperger Reg & Lazar Emese, 2008. "Option Valuation with Normal Mixture GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(2), pages 1-42, May.
    21. Duan, Jin-Chuan & Simonato, Jean-Guy, 2001. "American option pricing under GARCH by a Markov chain approximation," Journal of Economic Dynamics and Control, Elsevier, vol. 25(11), pages 1689-1718, November.

    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:387:y:2008:i:13:p:3192-3200. 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.