Abstract
As is widely known, the popular Black & Scholes model for option pricing suffers from systematic biases, as it relies on several highly questionable assumptions. In this paper we study the ability of neural networks (MLPs) in pricing call options on the S&P 500 index; in particular we investigate the effect of the hidden neurons in the in- and out-of-sample pricing. We modify the Black & Scholes model given the price of an option based on the no-arbitrage value of a forward contract, written on the same underlying asset, and we derive a modified formula that can be used for our purpose. Instead of using the standard backpropagation training algorithm we replace it with the Levenberg-Marquardt approach. By modifying the objective function of the neural network, we focus the learning process on more interesting areas of the implied volatility surface. The results from this transformation are encouraging.
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References
Bakshi, G., Cao, C., Chen, Z.: Empirical Performance of Alternative Options Pricing Models. Journal of Finance 52, 2003–2049 (1997)
Black, Scholes, M.: The Pricing of Options and Corporate Liabilities. Journal of Political Economy 81, 659–683 (1973)
Black, F., Scholes, M.: Fact and Fantasy in the Use of Options. Journal of Financial Analysts Journal 31, 36–41, 61-71 (1975)
Bollerslev, T., Engle, R.F., Nelson, D.B.: ARCH models. In: Engle, R.F., McFadden, D. (eds.) The Handbook of Econometrics, vol. 4, pp. 2959–3038. North-Holland, Amsterdam (1994)
Charalambous, C.: A Conjugate Gradient Algorithm for Efficient Training of Artificial Neural Networks. In: IEEE Proceedings-G, vol. 139, pp. 301–310 (1992)
Cont, R., Fonseca, J.: Dynamics of Implied Volatility Surfaces. Quantative Finance 2, 45–60
Cox, J.C., Ross, S.A., Stephen, A.: The Valuation of Options for Alternative Stochastic Processes. Journal of Financial Economics 3, 145–166 (1976)
Garcia, R., Gencay, R.: Pricing and Hedging Derivative Securities with Neural Networks and a Homogeneity Hint. Journal of Econometrics 94, 93–115 (2000)
Hull, J.C., White, A.: The Pricing of Options on Assets with Stochastic Volatilities. Journal of Finance 42, 281–300 (1987)
Hull, J.C.: Options, Futures and other Derivatives, 5th edn. Prentice Hall, New Jersey (2002)
Hutchinson, J.M., Lo, A.W., Poggio, T.: A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks. Journal of Finance 3, 851–889 (1994)
Jackwerth, C., Rubinstein, M.: Recovering Probabilities from Option Prices. Journal of Finance 51, 1611–1631 (1996)
Lajbcygier, P., Boek, C., Palaniswami, M., Flitman, A.: Neural Network Pricing of all Ordinaries SPI Options on Futures. In: Refenes, A. (ed.) Neural Networks in Financial Engineering, Proceedings of 3rd International Conference on Neural Networks in the Capital Markets, 1995, London, pp. 64–77. World Scientific, Singapore (1996)
Merton, R.C.: Option Pricing when Underlying Stock Returns are Discontinuous.Journal of Financial Economics, 125–144 (1976)
Merton, R.C.: Theory of Rational Option pricing. Bell Journal of Economics and Management Science 4, 141–183 (1973)
Yao, J., Li, Y., Tan, C.T.: Option Price Forecasting using Neural Networks. International Journal of Management Science (omega) 28, 455–466 (2000)
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Tzastoudis, V.S., Thomaidis, N.S., Dounias, G.D. (2006). Improving Neural Network Based Option Price Forecasting. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds) Advances in Artificial Intelligence. SETN 2006. Lecture Notes in Computer Science(), vol 3955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752912_38
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DOI: https://doi.org/10.1007/11752912_38
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