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.

Aims: This paper estimates working capital management on profit using logistic regression and discriminant analysis on manufacturing and industrial firms in Ghana. Study Design: Research Paper. Place and Duration of Study: Ghana, Secondary data for 2009 to 2014. Methodology: Data in the form of ratios were computed from the audited annual financial reports of 13 manufacturing and industrial firms listed on the Ghana Stock Exchange covering the period from 2009 to 2014.The ratios were used to determine the profitability of the firms. Results: The results showed that the logistic regression of the dependent variable (Profit) on the independent variables such as the Average Collection Period, the Inventory Conversion Period, the Average Payment Period, the Growth rate, the Debt Ratio, the Current Ratio and the Company Size were found to be significant and that there was no difference in variances for two firm classifications. This result implies that the linear discriminant function is effective in discriminating between a firm which effectively managed its working capital from one which did not. Conclusion: This study showed that the binary logistic regression model estimates correctly at least 75% of firm’s likelihood of managing working capital on profit while correctly discriminating the firms as having an effective management.