Stochastic volatility options pricing with wavelets and artificial neural networks

Abstract

The paper describes an alternative options pricing method which uses a binomial tree linked to an innovative stochastic volatility model. The volatility model is based on wavelets and artificial neural networks. Wavelets provide a convenient signal/noise decomposition of the volatility in the nonlinear feature space. Neural networks are used to infer future volatility from the wavelets feature space in an iterative manner. The bootstrap method provides the 95% confidence intervals for the options prices. Market options prices as quoted on the Chicago Board Options Exchange are used for performance comparison between the Black-Scholes model and a new options pricing scheme. The proposed dynamic volatility model produces as good as and often better options prices than the conventional Black-Scholes formulae.

Neural networks for option pricing and hedging: a literature review

“…The volatility is represented in terms of wavelets and the underlying modelled as a binomial tree. 10 These papers also derive prediction intervals for ANN estimates of option prices.11 This paper also treats the setup ofZapart [2002].…”

Forecasting Renminbi Exchange Rates Based on Autocorrelation Shell Representation and Neural Networks

“…Artificial neural networks (ANN) have been practiced recently in the area of time-series forecasting due to their flexibilities in data modeling , such as literatures [2]- [4]. In particular, Zapart [3] proposes an alternative way of looking at stochastic volatility models based haar wavelet and neural networks and their integration with conventional options pricing methods. Bai-Ling Zhang et al [4] proposed a hybrid neural-wavelet scheme to predict electricity demands in short term.…”

Neural Networks for Option Pricing and Hedging: A Literature Review

“…This paper also treats the setup ofZapart [2002]. 12 Correlations between underlyings.13 Coefficients for a linear regression that returns option prices.…”