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A Dynamic Weighted Distancedbased Fuzzy Time Series Neural Network with Bootstrap Model for Option Price Forecasting

Author

Listed:
  • Chih-Chung Yang

    (Department of Information Management, National Taiwan University of Science and Technology, Taiwan.)

  • Yungho Leu

    (Department of Information Management, National Taiwan University of Science and Technology, Taiwan.)

  • Chien-Pang Lee

    (Department of Information Management, Da-Yeh University, Taiwan.)

Abstract

The option price forecasting is still a big challenging problem because the option pricing is determined by many factors. Accordingly, it is difficult to predict option price accurately. To counter this problem, this paper proposes a novel hybrid model to forecast the option price. The proposed model, termed as the dynamic weighted distance-based fuzzy time series neural network with bootstrap model, is composed of a dynamic n-order 2-factor fuzzy time series model, a radial basis function neural network model and a bootstrap method. In the proposed model, the dynamic n-order 2-factor fuzzy time series model can automatic choose the best n-order for searching similar data from historical data and, then, build a training dataset for the radial basis function neural network model to forecast the option price. However, the sample size of option price data is small. Accordingly, this paper uses the bootstrap method to enhance the prediction accuracy of the proposed model. The experiment results show that the proposed model outperforms several existing methods in terms of RMSE, MAE and the testing results of Diebold-Marioano test.

Suggested Citation

  • 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.
    • Handle: RePEc:rjr:romjef:v::y:2014:i:2:p:115-129
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    References listed on IDEAS

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    1. Panda, Chakradhara & Narasimhan, V., 2007. "Forecasting exchange rate better with artificial neural network," Journal of Policy Modeling, Elsevier, vol. 29(2), pages 227-236.
    2. 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.
    3. Morariu, Nicolae & Iancu, Eugenia & Vlad, Sorin, 2009. "A Neural Network Model for Time-Series Forecasting," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 213-223, December.
    4. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    5. Yu, Hui-Kuang, 2005. "Weighted fuzzy time series models for TAIEX forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 609-624.
    6. 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.
    7. Mercuri, Lorenzo, 2008. "Option pricing in a Garch model with tempered stable innovations," Finance Research Letters, Elsevier, vol. 5(3), pages 172-182, September.
    8. 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.
    9. 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.
    10. Saman, Corina, 2011. "Scenarios of the Romanian GDP Evolution With Neural Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-140, December.
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    Cited by:

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    2. Mihaela SIMIONESCU, 2015. "The Accuracy Of Exchange Rate Forecasts In Romania," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 4(1), pages 54-64, JULY.

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    More about this item

    Keywords

    option price forecasting; fuzzy time series model; radial basis function neural network model; bootstrap method; Diebold-Marioano test;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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