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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Application of different forecasting methods for the volatility of industry indices |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2020 |
| Abstract Text | The relevance of measuring and predicting risk in the financial world has increased in recent years. With these demands on research, countless possibilities for volatility forecasts have been developed and applied to a wide range of historical financial data. The aim is to uncover differences in the forecasting accuracy of various forecasting methods for the volatility of industry indices and to find explanations for the results. The knowledge gained from this study can generate added value in the application of such forecasting models in risk and asset management. For this purpose, volatility forecasts for selected indices from different markets and industries are created and compared with each other. The models used are long-term average, simple moving average, weighted moving average, exponential weighted moving average, ARCH(1) and GARCH(1,1), whereby the analysis refers exclusively to in-sample forecasts. The industries examined for the selection of the indices are the automotive, biotechnology, pharmaceuticals, communications and energy and equivalents industries. For the ultimate performance evaluation, the mean absolute error is used, and two other measures, MAPE and RMSE, are discussed to illustrate the impact of the definition of error on the overall performance assessment. For the same reason, two additional measures are calculated in addition to the original benchmark and differences between the result are discussed. The results show a high performance of the exponential moving average, as well as the GARCH(1,1) over all industries considered. ARCH(1) has consistently delivered higher errors for the period 2014 to 2019, while moving averages deliver lower errors. Exponential moving average has outperformed all models across all industries and regions. Thus, a tendency of overspecification from GARCH(1,1) and ARCH(1) models have lead to poorer results than expected. Additionally, calculations based on constant volatility asssumptions as long-term average have proven to underperform due to their inability to capture long-term fluctuations in the financial market. |
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