ABSTRACT
The article starts with the traditional Black-Scholes(B-S) option pricing models. Three more models: Long-term and short-term memory networks(LSTM), support vector machine (SVM) and random forest(RF) are introduced to be compared to the B-S model and to each other on 50 ETF put option pricing. It is showed that each model has its advantages when used in different position. The neural network pricing result is better than that of the B-S model From the four evaluation indicators of MD, MSD, MAD and MPD, the absolute values of the four errors of the prediction results of the neural network are all smaller than the absolute values of the corresponding errors of the prediction results of the B-S model.
- Henrik Amilon. 2003. A neural network versus Black–Scholes: a comparison of pricing and hedging performances. Journal of Forecasting 22, 4 (2003), 317–335.Google ScholarCross Ref
- Henrik Amilon. 2003. A neural network versus Black–Scholes: a comparison of pricing and hedging performances. Journal of Forecasting 22, 4 (2003), 317–335.Google ScholarCross Ref
- Fischer Black and Myron Scholes. 2019. The pricing of options and corporate liabilities. In World Scientific Reference on Contingent Claims Analysis in Corporate Finance: Volume 1: Foundations of CCA and Equity Valuation. World Scientific, 3–21.Google Scholar
- Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5–32.Google ScholarDigital Library
- Christine A Brown and David M Robinson. 2002. Skewness and kurtosis implied by option prices: A correction. Journal of Financial Research 25, 2 (2002), 279–282.Google ScholarCross Ref
- F Dan Foresee and Martin T Hagan. 1997. Gauss-Newton approximation to Bayesian learning. In Proceedings of international conference on neural networks (ICNN’97), Vol. 3. IEEE, 1930–1935.Google ScholarCross Ref
- Tin Kam Ho. 1995. Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition, Vol. 1. IEEE, 278–282.Google Scholar
- Tin Kam Ho. 1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 8(1998), 832–844.Google ScholarDigital Library
- Robert W Kolb and James A Overdahl. 2003. Futures, options, and swaps. Vol. 5. Blackwell.Google Scholar
- Daewon Lee and Jaewook Lee. 2007. Equilibrium-based support vector machine for semisupervised classification. IEEE Transactions on Neural Networks 18, 2 (2007), 578–583.Google ScholarDigital Library
- Xiaosheng Liu and Zhibang Zhang. 2019. Parameter optimization of Support Vector Machine based on improved grid search method. IEEE Transactions on Neural Networks 40, 1 (2019), 5–9.Google Scholar
- Vladimir Vapnik, Esther Levin, and Yann Le Cun. 1994. Measuring the VC-dimension of a learning machine. Neural computation 6, 5 (1994), 851–876.Google Scholar
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