Volatility Forecasting using GARCH Processes with Exogenous Variables

Abstract: Volatility is a measure of the risk of an investment and plays an essential role in several areas of finance, including portfolio management and pricing of options. In this thesis, we have implemented and evaluated several so-called GARCH models for volatility prediction based on historical price series. The evaluation builds on different metrics and uses a comprehensive data set consisting of many assets of various types. We found that more advanced models do not, on average, outperform simpler ones. We also found that the length of the historical training data was critical for GARCH models to perform well and that the length was asset-dependent. Further, we developed and tested a method for taking exogenous variables into account in the model to improve the predictive performance of the model. This approach was successful for some of the large US/European indices such as Russell 2000 and S&P 500.