Exchange rate forecasting is hard issue for most of floating exchange rate economies. Studying exchange rate is very attractive matter since almost no model could beat random walk in short run yet. Relative yields and information in relative yield curves are contemporary topics in empirical literature and this study follows Chen and Tsang (2009) who model exchange rate changes with relative factors obtained from Nelson-Siegel (1987) yield curve model and find that relative factor model can forecast exchange rate change up to 2 years and perform better than random walk in short run. Analysis follows the methodology defined by Chen and Tsang (2009) and TL/USD, TL/EUR exchange rate changes are modeled by the relative factors namely relative level, relative slope and relative curvature. Basically, 162 weekly datasets from 09.01.2007 to 16.03.2010 are used and the relative factors for each week are estimated. Afterwards, regression analysis is made and results show that relative level and relative curvature factors are significant up to 4-6 weeks horizon but relative slope does not provide any valuable information for exchange rate prediction in Turkish financial market. Length of forecasting horizon of relative factor model is too short when compared to other exchange rate models. Since it is accepted that exchange rates follow random walk, we provided some tests to compare performance of the model. Similar to the literature, only short run performance of relative factor model is compared to random walk model and concluded that the relative factor model does not provide better forecasting performance in Turkish financial market
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12612505/index.pdf |
| Date | 01 September 2010 |
| Creators | Oz, Emrah |
| Contributors | Gaygisiz, Esma |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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