The Impact of Derivatives Use on Systemic Risk of Africa’s Banking System

ABSTRACT

This paper contributes to the debate about Africa’s financial stability by focusing on the prevalence of systemic risk in 40 listed derivatives user-banks over 2011–2017, employing the systemic risk index (SRI), and subsequently exploring how it links to the continent’s financial market development. The systemic risk buildup within the industry is mainly depicted as an unstable, diluted, and possibly depleting process. Gloomy predictions persist in the subsequent attempts to uncover plausible causes of systemic risk formation. Mainly, conventional defining factors for risk exhibit irrelevance toward systemic risk development, as threats to financial stability remain confined within the industry, supported by a lack of diversification in financial services. The investigated markets may be individually subject to internal vulnerabilities that could be exacerbated by financial shocks, feedback effects, and the likelihood of contagion and failure among financial institutions.

KEYWORDS:

• Derivatives use
• African banking market
• systemic risk contribution
• systemic risk sensitivity
• systemic risk index

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data used in this study come from a variety of data repositories, most of which are openly available to the public. The data is accessible online via the link: https://zenodo.org/record/6686031#.Y_y0vnZBzIU. The authors confirm that all data, including that generated by transforming original variables beforehand to meet the requirements of the chosen analytical methods, are available as additional materials with this article.

Notes

1 Efficiency gauges banks’ technical efficiency vis-à-vis their ability to extend credit to the business sector. Thereby, bias-corrected efficiency scores were derived using a VRS output-based DEA, based on an input‒output matrix comprising three inputs such as domestic deposits, total income and non-interest expenses whereas private credit (i.e. Gross loan minus Consumer loan) was modeled as an output.

2 This table shows the breakdown of the 40 African banks and number of observations in the final sample for each country.

3 Standard deviations are denoted as “SD.” The indices entail marginal measures of risk and may alternatively be expressed as percentages.

Akin to conventional risk metrics that capture downside risks, the calculated measures in this case are negative, even though they are conventionally reported as absolute values. Thus, the highest absolute measurement of downside risks implies the highest contribution or sensitivity to systemic risk (Leukes & Mensah, 2019).

4 P-values are provided in the parentheses below the coefficient estimates.

*, ** and *** respectively imply significance at 10%, 5% and 1% levels. The variable “sri” is the computed systemic risk index, while “D.capital,” “D.size,” “D.deposit,” and “D.marketcapitalisation” represent the respective first-difference equivalent for “capital,” “size,” “deposit flows,” and “market capitalization.”

5 P-values are provided in the parentheses below the coefficient estimates.

*, ** and *** respectively imply significance at 10%, 5% and 1% levels. Derivatives use was assumed endogenous for the GMM, which used the kaopen index as a proxy for financial openness. “sri” entails the systemic risk index. “D.capital,” “D.size,” “D.deposit,” and “D.marketcapitalisation” represent the respective first-difference equivalent for “capital,” “size,” “deposit flows,” and “market capitalization.” The dummy variables capturing ownership structure entail “localforeign_dummy” and “privatepublic_dummy,” distinguishing African from foreign banks and private from public institutions. “cons” represent the model’s constant.

6 Standard deviations are denoted as “SD.”

7 Standard deviations are denoted as “SD.”

8 Standard deviations are denoted as “SD.”

9 Standard deviations are denoted as “SD.”

10 Standard deviations are denoted as “SD.”

11 Standard deviations are denoted as “SD.”

Additional information

Funding

The Bradlow Foundation Research Funding for PhD students at the Wits Business School, and the Postgraduate Merit Award from the University of the Witwatersrand supported the work.