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題名 基于神經網路模型的台指選擇權定價實證分析
The Study of TXO Option Pricing Based on The Neural Network作者 唐寧
Tang, Ning貢獻者 廖四郎
Liao, Szu-Lang
唐寧
Tang,Ning關鍵詞 選擇權定價
深度學習
神經網路模型
Option pricing
Deep learning
Neural network model日期 2018 上傳時間 10-Jul-2018 15:34:36 (UTC+8) 摘要 在金融衍生性商品中，選擇權一直是一種重要的基礎性產品，因此選擇權定價一直學者研究的重點。近40年來選擇權發展中最重要的成果就是Black- Scholes 選擇權定價模型。然而由于該模型理想化的假設，導致它在真實定價過程中容易出現明顯的誤差。但是神經網路模型有著利用資料開始自我學習的特性，可以不用假設條件，單純由資料確定模型的結構和參數。 本文選取2008年到2018年的臺灣加權指數選擇權（TXO）日資料作爲研究對象，利用Python構建NN神經網路模型，將買權資料分爲買權完整資料、買權價內資料、買權價平資料、買權價外資料、買權上漲趨勢資料、買權下跌趨勢資料共6類資料。再加上賣權的6類資料，一共12大類資料。分別進行訓練模型。最後採用MSE、MAE兩種誤差指標來評價不同模型的預測精度。 最後發現NN神經網路模型的定價精度大多優于BS模型的期權定價效果。同時NN模型的價外選擇權資料的定價效果更精確，幷且按漲跌趨勢劃分後的選擇權資料定價效果也比完整資料的定價效果要好。
Option is a significant basic product in financial derivatives. How to price an option is a major issue to many scholars. During the last 40 years, Black-Scholes option pricing model has been considered as the crucial research achievement. However, obvious bias occurs in the real market pricing procedure due to the idealized assumption of this model. The neural network model has the characteristic of using data to start self-learning, so the structure and parameters of the model can be determined by data without assuming conditions. This thesis took TXO(2008-2018) as a research object, and used the Python to structure Neural Network(NN) model. Then the data of call option have been divided into 6 types , including‘all data’ ,‘in-the-price data’, ‘at-the-price data’, ‘out-the-price data’, ‘up-trend data’ and‘down-trend data’. The same classification is applied to the put option data. A total of 12 types of data have been trained by NN model separately. Finally, MSE and MAE are used to evaluate the accuracy of the forecasts of different models. In conclusion, the pricing accuracy of the neural network model is substantially better than that of the Black-Scholes model. Meanwhile , the pricing effect of out-the-price option data is more accurate, and the pricing of up-trend option data has a good effect either.參考文獻 [1] 李沃墻.(2000).台股重設型權證的評價績效比較陰.真理財金學 報,91-112 [2] 周大鵬. (2008). 基於B-P神經網路的期權定價研究. (Doctoral dissertation,中國人民大學). [3] 馬發強. (2012). 基於RBF神經網路的期權定價研究. (Doctoral dissertation, 中南大學). [4] 張鴻彥, & 林輝. (2007). 基于小波神經網絡的期權定價模型. 東南大學學報 (自然科學版), 37(4), 716-720. [5] 董瑩, 烏日嘎, & 齊淑華. (2013). 基於bp神經網路的期權定 價模型. 魯東大學學報(自然科學版), 29(3), 196-199. [6] 劉志强. (2005). 基于神經網路的期權定價模型. (Doctoral dissertation, 重慶大學). [7] 劉旭彬. (2011). 基於神經網路方法的期權定價應用研究. (Doctoral dissertation, 暨南大學). [8] 譚朵朵. (2008). 基於bp神經網路的s&p500指數期權定價. 統 計與資訊理論壇, 23(11), 40-43. [9] Amilon, H. (2003). A neural network versus black– scholes: a comparisonof pricing and hedging performances.Journal of Forecasting, 22(4), 317-335. [10] Gençay, R., & Qi, M. (2001). Pricing and hedging derivative securities with neural networks: bayesian regularization, early stopping, and bagging. IEEE Trans Neural Netw, 12(4), 726-734. [11] Hinton, G. E. (2012). A practical guide to training restricted Boltzmann machines. In Neural networks: Tricks of the trade(pp. 599-619). Springer, Berlin, Heidelberg. [12] Huang, S. C., & Wu, T. K. (2006, September). A hybrid unscented Kalman filter and support vector machine model in option price forecasting. In International Conference on Natural Computation (pp. 303-312). Springer, Berlin, Heidelberg. [13] Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554. [14] Hutchinson, J. M., Lo, A. W., & Poggio, T. (1994). A nonparametric approach to pricing and hedging derivative securities via learning networks. Journal of Finance, 49(3), 851-889. [15] Liang, X., Zhang, H., Xiao, J., & Chen, Y. (2009). Improving option price forecasts with neural networks and support vector regressions. Neurocomputing, 72(13), 3055-3065. [16] Panayiotis, A. C., Spiros, M. H., & Chris, C. (2004, July). Option pricing and trading with artificial neural networks and advanced parametric models with implied parameters. In Neural Networks, 2004. proceedings. 2004 IEEE International Joint Conference on (Vol. 4, pp. 2741-2746). IEEE. [17] Park, H., Kim, N., & Lee, J. (2014). Parametric models and non-parametric machine learning models for predicting option prices: empirical comparison study over kospi 200 index options. Expert Systems with Applications, 41(11), 5227-5237. [18] Paul R. Lajbcygier, & Jerome T. Connor. (1997). Improved option pricing using artificial neural networks and, bootstrap methods. International Journal of Neural Systems, 8(04), 457-471. [19] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533. [20] Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015). Highway networks. arXiv preprintarXiv:1505.00387. [21] Wang, Y. H. (2009). Nonlinear neural network forecasting model for stock index option price: Hybrid GJR–GARCH approach. Expert Systems with Applications, 36(1), 564-570. [22] Wu, S., Zhong, S., & Liu, Y. (2018). Deep residual learning for image steganalysis. Multimedia tools and applications, 77(9), 10437-10453. 描述 碩士
國立政治大學
金融學系
105352041資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105352041 資料類型 thesis dc.contributor.advisor 廖四郎 zh_TW dc.contributor.advisor Liao, Szu-Lang en_US dc.contributor.author (Authors) 唐寧 zh_TW dc.contributor.author (Authors) Tang,Ning en_US dc.creator (作者) 唐寧 zh_TW dc.creator (作者) Tang, Ning en_US dc.date (日期) 2018 en_US dc.date.accessioned 10-Jul-2018 15:34:36 (UTC+8) - dc.date.available 10-Jul-2018 15:34:36 (UTC+8) - dc.date.issued (上傳時間) 10-Jul-2018 15:34:36 (UTC+8) - dc.identifier (Other Identifiers) G0105352041 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118537 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 105352041 zh_TW dc.description.abstract (摘要) 在金融衍生性商品中，選擇權一直是一種重要的基礎性產品，因此選擇權定價一直學者研究的重點。近40年來選擇權發展中最重要的成果就是Black- Scholes 選擇權定價模型。然而由于該模型理想化的假設，導致它在真實定價過程中容易出現明顯的誤差。但是神經網路模型有著利用資料開始自我學習的特性，可以不用假設條件，單純由資料確定模型的結構和參數。 本文選取2008年到2018年的臺灣加權指數選擇權（TXO）日資料作爲研究對象，利用Python構建NN神經網路模型，將買權資料分爲買權完整資料、買權價內資料、買權價平資料、買權價外資料、買權上漲趨勢資料、買權下跌趨勢資料共6類資料。再加上賣權的6類資料，一共12大類資料。分別進行訓練模型。最後採用MSE、MAE兩種誤差指標來評價不同模型的預測精度。 最後發現NN神經網路模型的定價精度大多優于BS模型的期權定價效果。同時NN模型的價外選擇權資料的定價效果更精確，幷且按漲跌趨勢劃分後的選擇權資料定價效果也比完整資料的定價效果要好。 zh_TW dc.description.abstract (摘要) Option is a significant basic product in financial derivatives. How to price an option is a major issue to many scholars. During the last 40 years, Black-Scholes option pricing model has been considered as the crucial research achievement. However, obvious bias occurs in the real market pricing procedure due to the idealized assumption of this model. The neural network model has the characteristic of using data to start self-learning, so the structure and parameters of the model can be determined by data without assuming conditions. This thesis took TXO(2008-2018) as a research object, and used the Python to structure Neural Network(NN) model. Then the data of call option have been divided into 6 types , including‘all data’ ,‘in-the-price data’, ‘at-the-price data’, ‘out-the-price data’, ‘up-trend data’ and‘down-trend data’. The same classification is applied to the put option data. A total of 12 types of data have been trained by NN model separately. Finally, MSE and MAE are used to evaluate the accuracy of the forecasts of different models. In conclusion, the pricing accuracy of the neural network model is substantially better than that of the Black-Scholes model. Meanwhile , the pricing effect of out-the-price option data is more accurate, and the pricing of up-trend option data has a good effect either. en_US dc.description.tableofcontents 第一章、緒論 1 第一節、研究背景 1 第二節、研究方法與目的 2 第三節、論文架構 3 第二章、文獻回顧 4 第一節、選擇權定價的理論發展 4 第二節、神經網路模型的相關方法 7 第三節、神經網路模型在選擇權定價中的應用 11 第三章、神經網路模型的設計 13 第一節、樣本資料的選擇 13 第二節、Black-Scholes模型設計 14 第三節、神經網路模型（NN）的設計 19 第四章、實證過程 26 第一節、買權資料訓練過程 26 第二節、賣權資料訓練過程 34 第三節、上漲趨勢資料和下跌趨勢資料分別的訓練過程 41 第四節、Black-Scholes公式的測試過程 47 第五章、實證結果與分析 50 第一節、實證結果 50 第二節、結果分析 52 第三節、研究展望 53 參考文獻 55 zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105352041 en_US dc.subject (關鍵詞) 選擇權定價 zh_TW dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) 神經網路模型 zh_TW dc.subject (關鍵詞) Option pricing en_US dc.subject (關鍵詞) Deep learning en_US dc.subject (關鍵詞) Neural network model en_US dc.title (題名) 基于神經網路模型的台指選擇權定價實證分析 zh_TW dc.title (題名) The Study of TXO Option Pricing Based on The Neural Network en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] 李沃墻.(2000).台股重設型權證的評價績效比較陰.真理財金學 報,91-112 [2] 周大鵬. (2008). 基於B-P神經網路的期權定價研究. (Doctoral dissertation,中國人民大學). [3] 馬發強. (2012). 基於RBF神經網路的期權定價研究. (Doctoral dissertation, 中南大學). [4] 張鴻彥, & 林輝. (2007). 基于小波神經網絡的期權定價模型. 東南大學學報 (自然科學版), 37(4), 716-720. [5] 董瑩, 烏日嘎, & 齊淑華. (2013). 基於bp神經網路的期權定 價模型. 魯東大學學報(自然科學版), 29(3), 196-199. [6] 劉志强. (2005). 基于神經網路的期權定價模型. (Doctoral dissertation, 重慶大學). [7] 劉旭彬. (2011). 基於神經網路方法的期權定價應用研究. (Doctoral dissertation, 暨南大學). [8] 譚朵朵. (2008). 基於bp神經網路的s&p500指數期權定價. 統 計與資訊理論壇, 23(11), 40-43. [9] Amilon, H. (2003). A neural network versus black– scholes: a comparisonof pricing and hedging performances.Journal of Forecasting, 22(4), 317-335. [10] Gençay, R., & Qi, M. (2001). Pricing and hedging derivative securities with neural networks: bayesian regularization, early stopping, and bagging. IEEE Trans Neural Netw, 12(4), 726-734. [11] Hinton, G. E. (2012). A practical guide to training restricted Boltzmann machines. In Neural networks: Tricks of the trade(pp. 599-619). Springer, Berlin, Heidelberg. [12] Huang, S. C., & Wu, T. K. (2006, September). A hybrid unscented Kalman filter and support vector machine model in option price forecasting. In International Conference on Natural Computation (pp. 303-312). Springer, Berlin, Heidelberg. [13] Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554. [14] Hutchinson, J. M., Lo, A. W., & Poggio, T. (1994). A nonparametric approach to pricing and hedging derivative securities via learning networks. Journal of Finance, 49(3), 851-889. [15] Liang, X., Zhang, H., Xiao, J., & Chen, Y. (2009). Improving option price forecasts with neural networks and support vector regressions. Neurocomputing, 72(13), 3055-3065. [16] Panayiotis, A. C., Spiros, M. H., & Chris, C. (2004, July). Option pricing and trading with artificial neural networks and advanced parametric models with implied parameters. In Neural Networks, 2004. proceedings. 2004 IEEE International Joint Conference on (Vol. 4, pp. 2741-2746). IEEE. [17] Park, H., Kim, N., & Lee, J. (2014). Parametric models and non-parametric machine learning models for predicting option prices: empirical comparison study over kospi 200 index options. Expert Systems with Applications, 41(11), 5227-5237. [18] Paul R. Lajbcygier, & Jerome T. Connor. (1997). Improved option pricing using artificial neural networks and, bootstrap methods. International Journal of Neural Systems, 8(04), 457-471. [19] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533. [20] Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015). Highway networks. arXiv preprintarXiv:1505.00387. [21] Wang, Y. H. (2009). Nonlinear neural network forecasting model for stock index option price: Hybrid GJR–GARCH approach. Expert Systems with Applications, 36(1), 564-570. [22] Wu, S., Zhong, S., & Liu, Y. (2018). Deep residual learning for image steganalysis. Multimedia tools and applications, 77(9), 10437-10453. zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.MB.014.2018.F06 -
