This relationship between access to financing and firm characteristics was studied. The authors used data from a survey of small and medium-sized enterprises from Mauritius, an island economy that is part of the sub-Saharan African continent. They examined the factors that affect the ability of firms to gain access to financing. Using the principal component analysis, the authors group variables affecting access to financing in four main components including working capital constraint, the external constraint, the size constraint, and the age constraint. The authors show that these components are important factors that influence the ability of firms to gain access to financing.

This paper examines whether women entrepreneurs operating in the informal sector in the small island economy of Mauritius have been affected by the global financial crisis, an area which is largely under-researched due to data limitations. Survey data of 158 women entrepreneurs operating in the informal sector in Mauritius is used to assess the impact of the financial crisis on their business activities. The principal component analysis is applied and our findings reveal that women entrepreneurs have been affected by the global financial crisis in two ways. The first is that they have been affected through revenue factors, which comprise the following: a fall in demand, a drop in revenue and a decrease in profits. The second way in which women entrepreneurs in the informal sector have been affected is through cost factors associated with a rise in the price of raw materials and a rise in other costs of production.

No abstract

Risk permeates more and more financial markets around the world. It is an essential element that all financial market actors attempt to model and manage. This paper proposes a novel model that improves the predictive accuracy of high frequency volatility forecasts. The ANN‐MC‐GARCH model is therefore developed in this research. The hybrid model enhances the MC‐GARCH conditional variance model by including endogenous market variables in a feed forward network which models volatility in terms of past disturbances and variances. The forecasting accuracy of the novel ANN‐MC‐GARCH is evaluated against the classical MC‐GARCH model. The empirical investigation employs the 1‐min high frequency observations of four exchange rates (USD/EUR, USD/GBP, USD/JPY & AUD/JPY), three market indices (France 40, UK 100 & USA 500) and two metal commodity indices (Spot Gold & Spot Silver). The backtesting method employs the RMSE and MAE performance metrics, the out‐of‐sample , the Diebold‐Mariano test and the Model Confidence Set (MCS) procedure. The empirical findings show that the hybrid model is superior to the MC‐GARCH model for the nine datasets.

In this paper bivariate modelling methodology, solely applied to the spot price of electricity or demand for electricity in earlier studies, is extended to a bivariate process of spot price of electricity and demand for electricity. The suggested model accommodates common idiosyncrasies observed in deregulated electricity markets such as cyclical trends in price and demand for electricity, occurrence of extreme spikes in prices, and mean-reversion effect seen in settling of prices from extreme values to the mean level over a short period of time. The paper presents detailed statistical analysis of historical data of daily averages of electricity spot prices and corresponding demand for electricity. The data is obtained from the NSW section of Australian Energy Markets.