Option strategies using hybrid Support Vector Regression - ARIMA

Sammanfattning: In this thesis, the use of machine learning in option strategies is evaluated with focus on the S&P 500 Index. The first part of the thesis focuses on testing the performance power of the Support Vector Regression (SVR) method for the historical realized volatility with a window of 20 days. The prediction window will also be 1-month forward (approximately 20 trading days). The second part of the thesis focuses on creating an ARIMA model that forecasts the error that is based on the difference between the predicted respective true values. This is done in order to create the hybrid SVR-ARIMA model. The new model now consists of a realized volatility value derived from the SVR model as well as the error obtained from the ARIMA model. Lastly, the two methods, that is single SVR and hybrid SVR-ARIMA are compared and the model that exhibits the best result is used within two option strategies. The results showcase the promising forecasting power of the SVR method which for this dataset had an accuracy leveland 67 % for the realized volatility. The ARIMA model also exhibits successful forecasting ability for the next lag. However, for this dataset, the Hybrid SVR-ARIMA model outperforms the single SVR model. It is debatable whether the success of these methods may be due to the fact the dataset only covers the years between 2010-2018 and the highly volatile environments of the financial crisis 2008 is omitted. Nonetheless, the use of the hybrid SVR-ARIMA model used within the two option strategies gives an average payoff 0.37 % and 1.68 %. It should however be noted that the affiliated costs of trading options is not included in the payoff and neither is the cost of premium due in buying options as the costs vary depending on the origin of the purchase. This thesis has been performed in collaboration with Crescit Asset Management in Stockholm, Sweden.