This thesis examines the predictive ability of models for forecasting inflation and financial market volatility. Emphasis is put on evaluation of forecasts and the usage of large data sets. Variety of models are used to forecast inflation, including diffusion indices, artificial neural networks, and traditional linear regressions. Financial market volatility is forecast using various GARCH-type and high-frequency based models. High-frequency data are also used to obtain ex-post estimates of volatility, which is then used to evaluate forecasts. All forecast are evaluated using recently proposed techniques that can account for data snooping bias, nested, and nonlinear models.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.85018 |
| Date | January 2004 |
| Creators | Kışınbay, Turgut |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Doctor of Philosophy (Department of Economics.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 002173864, proquestno: AAINR06312, Theses scanned by UMI/ProQuest. |
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