Abstract:
This thesis provides a robust analysis of volatility forecasting of Euro-ETB exchange rate
using weekly data spanning the period January 3, 2000 to December 2, 2015.The forecasting
performance of various GARCH-type models is investigated based on forecasting performance
criteria such as MSE and MAE based tests, and alternative measures of realized volatility. To
our knowledge, this is the first study that focuses on Euro-ETB exchange rate using high
frequency data, and a range of econometric models and forecast performance criteria.The
empirical results indicate that the Euro-ETB exchange rate series exhibits persistent volatility
clustering over the study period. We document evidence that ARCH (8), GARCH (1, 1),
EGARCH (1, 1) and GJR-GARCH (2, 2) models with normal distribution, student’s-t
distribution and GED are the best in-sample estimation models in terms of the volatility
behavior of the series. Amongst these models, GJR-GARCH (2, 2) and GARCH(1,1) with
students t- distribution are found to perform best in terms of one step-ahead forecasting based
on realized volatility calculated from the underlying daily data and squared weekly first
difference of the logarithm of the series,respectively. Furthermore, GJR-GARCH (2, 2) with
student’s t-distribution is the best model both interms of fit the stylized facts(like asymmetric)
and forecasting performance of the volatility of Ethiopian Birr/ Euro exchange rate among
others.
