The model uncertainty is a phenomenon where general consensus about the form of specific model is unclear. Stock returns perfectly meet this condition, as extensive literature offers diverse methods and potential drivers without a clear winner among them. Relatively recently, averaging techniques emerged as a possible solution to such scenarios. The two major averaging branches, Bayesian (BMA) and Frequentist (FMA) averaging, naturally deal with uncertainty by averaging over all model candidates rather than choosing the "best" one of them. We focus on FMA and apply this method to our data from U.S. market about S&P 500 index, that I help to explain with the set of eleven explanatory variables chosen in accordance with related literature. To preserve a real-world applicability, I use rolling window scheme to regularly update data in the fitting model for quarterly based re- estimation. Consequently, predictions are obtained with the use of most recent data. Firstly, we find out that simple historical average model can be beaten with a standard model selection approach based on AIC value, with variables as Dividend Yield, Earnings ratio, and Book-to-Market value proving consistently as most significant across quarterly models. With FMA techniques, I was not able to consistently beat the benchmark...
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:397982 |
| Date | January 2019 |
| Creators | Pacák, Vojtěch |
| Contributors | Havránek, Tomáš, Špolcová, Dominika |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
Page generated in 0.0019 seconds