Modeling the Relation Between Implied and Realized Volatility
Abstract: Options are an important part in today's financial market. It's therefore of high importance to be able to understand when options are overvalued and undervalued to get a lead on the market. To determine this, the relation between the volatility of the underlying asset, called realized volatility, and the market's expected volatility, called implied volatility, can be analyzed. In this thesis five models were investigated for modeling the relation between implied and realized volatility. The five models consisted of one Ornstein–Uhlenbeck model, two autoregressive models and two artificial neural networks. To analyze the performance of the models, different accuracy measures were calculated for out-of-sample forecasts. Signals from the models were also calculated and used in a simulated options trading environment to get a better understanding of how well they perform in trading applications. The results suggest that artificial neural networks are able to model the relation more accurately compared to more traditional time series models. It was also shown that a trading strategy based on forecasting the relation was able to generate significant profits. Furthermore, it was shown that profits could be increased by combining a forecasting model with a signal classification model.
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