Kwabena Asare

Abstract
This study used co-integrated analysis and Granger causality test in modelling the Growth Domestics Products (GDP) of Ghana with other three selected macroeconomic such as Foreign Direct Investment (FDI), inflation and real exchange rate for the period of 1980 to 2013. Data were taken from the World Bank’s World Development Indicators and Bank of Ghana. The objectives of the study are to examine both long-run relationships and direction of causality between the GDP and the macroeconomic variables. The time series properties of the data were, first, analysed using the Augmented Dickey-Fuller (ADF) test and KPSS test. The empirical results derived indicate that all the variables were stationary after their first differencing; i.e. variables are integrated of order one. The study further established that there is co-integration between the selected macroeconomic variables and GDP in Ghana indicating long run relationship. The above long term relation indicates that exchange rate and foreign direct investments have a negative effect on GDP whiles Inflation (CPI) showed a positive effect on GDP. The study further investigated the causal relationship using the Granger Causality analysis, which indicates a unidirectional causality between GDP growth rate and exchange rate and bidirectional causality between Inflation rate and Exchange, and also between Inflation rate and GDP, whiles FDI does not granger cause Inflation rate, exchange rate, GDP and visa versa in Ghana for the study period at 5%.

Modelling of extreme events has always been of interest in fields such as hydrology and meteorology. However, after the recent global financial crises, appropriate models for modelling of such rare events leading to these crises have become quite essential in the finance and risk management fields. This paper models the extreme values of the Ghana stock exchange all-shares index (2000–2010) by applying the extreme value theory (EVT) to fit a model to the tails of the daily stock returns data. A conditional approach of the EVT was preferred and hence an ARMA-GARCH model was fitted to the data to correct for the effects of autocorrelation and conditional heteroscedastic
terms present in the returns series, before the EVT method was applied. The Peak Over Threshold approach of the EVT, which fits a Generalized Pareto Distribution (GPD) model to excesses above a certain selected threshold, was employed. Maximum likelihood estimates of the model parameters were obtained and the model’s goodness of fit was assessed graphically using Q–Q, P–P and density plots. The findings indicate that the GPD provides an adequate fit to the data of excesses. The size of the extreme daily Ghanaian stock market movements were then computed using the value at risk and expected shortfall risk measures at some high quantiles, based on the fitted GPD
model.