Impact of macroeconomic announcements on foreign exchange volatility: Evidence from South Africa

This study focuses on scheduled macroeconomic news announcements and evaluates their impact on the volatility of the South African rand (ZAR) and US dollar (USD) exchange rate using high frequency data. The following asymmetries are studied: news items by geographical location, no-news versus surprise news announcements and positive versus negative news announcements. We make the following findings in our empirical study: (i) After the release of a news announcement, the level of foreign exchange volatility rises. This is independent of whether the news item surprised the market or not.

Nonlinearities in Financial Development–Economic Growth Nexus: Evidence from sub–Saharan Africa (SSA)

The impact of financial development on economic growth has received much attention in recent literature. However, there are potential discontinuities mediating finance–growth nexus that existing empirical studies have not rigorously examined. This study investigates whether the impact of finance on economic growth is conditioned on the initial levels of countries’ income per capita, human capital and financial development for 29 sub–Saharan Africa countries over the period 1980–2014 using a sample splitting and threshold estimation technique.

Antitrust market definition using statistical learning techniques and consumer characteristics

Market definition is the first step in an antitrust case and relies on empirical evidence of substitution patterns. Cross-price elasticity estimates are preferred evidence for studying substitution patterns, due to advances in IO econometric modelling. However, the data and time requirements of these models weigh against their universal adoption for market definition purposes. These practical constraints — and the need for a greater variety of evidence — lead practitioners to rely on a larger set of less sophisticated tools for market definition.

Adaptive Bayesian Analysis for Binomial Proportions

We consider the problem of statistical inference of binomial proportions for non-matched, correlated samples, under the Bayesian framework. Such inference can arise when the same group is observed a different number of times on two or more inference occasions, with the aim of testing the proportion of some trait. These scenarios can occur when we are interested to infer the proportion of extreme wave height per year, at a certain measuring station, where measurements are made every hour.

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