Option Pricing Using Artificial Neural Networks : an Australian Perspective

Tobias Hahn

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Stochastic processes and financial applications Financial Risk and Volatility Modeling Stock Market Forecasting Methods

Abstract

The thesis addresses the question of how option pricing can be improved using machine learning techniques. The focus is on the modelling of volatility, the central determinant of an option price, using artificial neural networks. This is done explicitly as a volatility forecast and its accuracy evaluated. In addition, its use in option pricing is tested and compared with a direction option pricing approach.

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References

1973The Pricing of Options and Corporate Liabilities 1970EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK*1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1993A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options 2005Theory of rational option pricing 1979Option pricing: A simplified approach 1991Efficient Capital Markets: II

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2021Artificial Neural Networks Performance in WIG20 Index Options Pricing

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