The Use of Artificial Neural Network in Option Pricing: The Case of S and P 100 Index Options
Due to the increasing and varying risks that economic units face with, derivative instruments gain substantial importance, and trading volumes of derivatives have reached very significant level. Parallel with these high trading volumes, researchers have developed many different models. Some are parametric, some are nonparametric. In this study, the aim is to analyse the success of artificial neural network in pricing of options with S&P 100 index options data. Generally, the previous studies cover the data of European type call options. This study includes not only European call option but also American call and put options and European put options. Three data sets are used to perform three different ANN models. One only includes data that are directly observed from the economic environment, i.e. strike price, spot price, interest rate, maturity, type of the contract. The others include an extra input that is not an observable data but a parameter, i.e. volatility. With these detail data, the performance of ANN in put/call dimension, American/European dimension, moneyness dimension is analyzed and whether the contribution of the volatility in neural network analysis make improvement in prediction performance or not is examined. The most striking results revealed by the study is that ANN shows better performance when pricing call options compared to put options; and the use of volatility parameter as an input does not improve the performance.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1060147Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2570
 F. Black and M. Scholes, "The pricing of options and corporateliabilites," Journal of Political Economy, vol. 81, no. 3, pp.637-654, 1973.
 R.C. Merton, "Option pricing when underlying stock returns are discontinuos," Journal of Financial Economics, vol. 3, pp. 125-144, 1975.
 J.C. Hull and A. White, "The pricing of options on assets with stochastic volatilites," The Journal Of Finance, vol. 42, no. 2, 281- 300, 1987.
 K. I. Amin and V. K. Ng, "Option valuation with systematic stochastic volatility," The Journal of Finance, vol. 48, no. 3, pp. 881- 910, 1993.
 J. Duan, "The Garch option pricing model," Mathematical Finance, vol.5, no. 1,pp. 13-32, 1995.
 J. Duan and H. Zhang, "Pricing Hang Seng Index options around the Asian financial crisis- a Garch approach," Journal of Banking & Finance, vol. 25, pp. 1989-2014, 2001.
 L. O. Scott, "Pricing stock options in a jump-diffusion model with stochastic volatility and interest Rate," Mathematical Finance, vol. 7, no. 4, pp. 413-424, 1997.
 M. Malliaris and L. Salchenberger, "A neural network model for estimating option prices," Journal of Applied Intelligence, vol. 3, pp. 193-206, 1993.
 J.M. Hutchidson, A. W. Lo and T., Poggio, "A nonparametric approach to pricing and hedging derivative securities via learning networks," The Journal of Finance, vol. 49, no. 3, 851-889, 1994.
 J. Yao, Y. Li and C. L. Tan, "Option price forecasting using neural networks," Omega, vol. 28, pp. 455-466, 1999.
 H. Amilon, "A neural network versus Black-Scholes: a comparison of pricing and hedging Performances", Journal of Forecasting, vol. 22, pp. 317-335, 2003.
 T. Daglish, "A pricing and hedging comparison of parametric and nonparametric approaches for American Index Options," Journal of Financial Econometrics, vol. 1, no. 3, pp. 327-364, 2003.
 J. Bennell and C. Sutcliffe, "Black-Sholes versus artificial neural networks in pricing FTSE 100 options," Intelligent Systems In Accounting, Finance and Management, vol. 12, pp. 243-260, 2004.
 U. Anders, O. Korn and C. Schmitt, "Improving the pricing of options: a neural network approach," Journal of Forcasting, vol. 17, pp. 369-388, 1998.
 R. Garcia and R.Gen├ºay, "Pricing and hedging derivative securities with neural networks and homogenity hint," Journal of Econometrics, vol. 94, pp. 93-115, 2000.
 S. Kahraman, O. Gunaydin, M. Alber and M. Fener, "Evaluating the strength and deformability properties of misis fault breccia using artificial neural networks," Expert Systems with Applications, vol. 36, pp. 6874-6878, 2009.
 P. Gupta and N. K. Sinha, "An improved approach for nonlinear system identification using neural networks," Journal of the Franklin Institute, vol. 336, no. 4, pp. 721-734, 1999.
 J. Johnson and P. Picton,"Mechatronics: Designing Intelligent Machines,Concepts in Artificial Intelligence," Butterworth- Heinemann vol. 2, 1995.
 E. Koskivaara, "Neural networks in analytical review procedures," Managerial Auditing Journal, vol. 19, no.2, pp. 191-223, 2004.
 K.L. Choya, W.B. Leea and V. Lob, "Design of an intelligent supplier relationship managementsystem: a hybrid case based neural network approach" Expert Systems with Applications, vol. 24,pp. 225-237, 2003.
 T.C. Tang and L.C. Chi, "Neural networks analysis in business failure prediction of Chinese importers: A between-countries approach" Expert Systems with Applications, vol. 29, pp. 244-255, 2005.