This paper departs from the traditional aid–economic growth studies through its examination of the impact of aid and its volatility on sectoral growth by relying on panel dataset of 37 sub-Saharan African (SSA) countries for the period 1980–2014. Findings from our system generalised methods of moments (GMM) show that, while foreign aid significantly drives economic transformation, aid volatility deteriorates sectoral value additions with huge impact on the non–tradable sector and a no apparent effect on the agricultural sector.
The role of financial sector development in economic volatility has been extensively studied albeit without informative results largely on the failure of extant studies to decompose volatility into its various components. By disaggregating volatility, this study examines the effect of financial development on volatility as well as channels through which finance affects volatility components in 23 sub-Saharan African countries over the period 1980–2014 using the newly developed panel cointegration estimation strategy.
This paper examines whether accounting for structural changes in the conditional variance process, through the use of Markov-switching models, improves estimates and forecasts of stock return volatility over those of the more conventional single-state (G)ARCH models, within and across selected African markets for the period 2002-2012. In the univariate portion of the paper, the performances of various Markov-switching models are tested against a single-state benchmark model through the use of in-sample goodness-of-fit and predictive ability measures.