Computationally efficient FLANN-based intelligent stock price prediction system
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Application notes: dynamic physical behavior analysis for financial trading decision support
IEEE Computational Intelligence Magazine
A learning-based contrarian trading strategy via a dual-classifier model
ACM Transactions on Intelligent Systems and Technology (TIST)
Pricing And Hedging Short Sterling Options Using Neural Networks
International Journal of Intelligent Systems in Accounting and Finance Management
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In empirical modeling, there have been two strands for pricing in the options literature, namely the parametric and nonparametric models. Often, the support for the nonparametric methods is based on a benchmark such as the Black-Scholes (BS) model with constant volatility. In this paper, we study the stochastic volatility (SV) and stochastic volatility random jump (SVJ) models as parametric benchmarks against feedforward neural network (FNN) models, a class of neural network models. Our choice for FNN models is due to their well-studied universal approximation properties of an unknown function and its partial derivatives. Since the partial derivatives of an option pricing formula are risk pricing tools, an accurate estimation of the unknown option pricing function is essential for pricing and hedging. Our findings indicate that FNN models offer themselves as robust option pricing tools, over their sophisticated parametric counterparts in predictive settings. There are two routes to explain the superiority of FNN models over the parametric models in forecast settings. These are nonnormality of return distributions and adaptive learning