# International Economics

## Abstract

The global foreign exchange market is undoubtedly the world's biggest market with huge

trading volume, surpassing other markets including equities and commodities. This study focuses on

exchange rate modelling where we perform an empirical study to evaluate models which can be used

to identify a common Value at Risk (VaR) model for fourteen African currencies. The descriptive

statistics of our data reveal the salient features common to financial time series which are nonnormality,

high kurtosis, skewness and presence of heteroscedasticity except for one currency, the

central African CFA Franc. The latter is excluded from the modelling exercise. We make use of

GARCH, GJR-GARCH and FIGARCH to model volatility using four distributions: normal, student-t,

GED and skew-t. Unconditional EVT and dynamic GARCH-EVT methodologies are also used for

volatility modelling; both with static (S) and rolling windows (R). Results show that static window

shows a better performance than rolling window. Unconditional EVT is seen to overpredict VaR and

dynamic EVT is not among the best models. The GARCH (33.3%) and GJR-GARCH (38.5%) models

produce better forecasts with a dominance for GJR-GARCH models. Despite the data being skewed,

the normal distribution gives better forecast. We also observe that GARCH-S-Normal is suitable for

Southern African Development Community (SADC) and FIGARCH for East African Community

(EAC) countries. A geographical combination reveals the use of GJR-GARCH for Northern and

Western African regions and GARCH-S-Normal for South African region. Despite not finding a unique

model for all countries, it is interesting to note that different regions/communities can adopt a common

Value at Risk model for forecasting purposes. Our results provide a full validation of the models under

the different backtesting methods and thus could be implemented at the practitioner’s level.

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