The thesis addresses the question of how option pricing can be improved usingmachine 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. A review of existing literature demonstrated a lack of clarity with respect to the model development methodology used in the area. This issue is discussed and finally addressed along with a consolidation of the various modelling app roaches undertaken previously by researchers in the field. To this end, a consistent process is developed to guide the specific model development. Previous research has focused on index options, i.e. a single time series and some options related to it. The aim of the research presented here was to extend this to equity options, taking into consideration the particular characteristics of the underlying and the options.The research focuses on the Australian equity option market before and after the global financial crisis. The results suggest that in the market and over the time frame studied, an explicit volatility model combined with existing deterministic models is preferable.Beyond the specific results of the study, a detailed discussion of the limitations and methodological issues is presented. These relate not only to the methodology used here but the various choices and trade-offs faced whenever machine learning techniques are used for volatility or option price modelling. Academic insight as well as practical applications depend critically on the understanding of these choices.
|Date of Award||8 Feb 2014|
|Supervisor||Bruce Vanstone (Supervisor) & Gavin Finnie (Supervisor)|