Modelling systemic risk in the South African banking sector using CoVaR

Mathias Manguzvane, John Weirstrass Muteba Mwamba

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

In this paper, we model systemic risk by making use of the conditional quantile regression to identify the most systemically important and vulnerable banks in South Africa (SA). We measure the marginal contributions of each bank to systemic risk by computing the difference between system risk of individual banks when they are in a normal state and when they are in distress. Using daily stock market prices of six SA commercial banks1 from June 2007 to April 2016; Our results show a considerable increase in the market risk of all the six banks during the 2008 global financial crisis with African Bank being the riskiest bank in the country. Our systemic risk ranking shows that First Rand Bank was the largest contributor to systemic risk followed by Standard Bank, Barclays Africa, Nedbank, Capitec and lastly African Bank. These results suggest that the largest banks pose a bigger threat to the banking system than the smaller banks. These findings clearly indicate that different banks pose different threats to the banking system and the economy at large. Hence, specific actions that go beyond limiting idiosyncratic risk are needed if stability is to be attained through macro prudential regulation.

Original languageEnglish
Pages (from-to)624-641
Number of pages18
JournalInternational Review of Applied Economics
Volume33
Issue number5
DOIs
Publication statusPublished - 3 Sept 2019

Keywords

  • Conditional quantile
  • banking sector
  • conditional value at risk
  • systemic risk

ASJC Scopus subject areas

  • Economics and Econometrics

Fingerprint

Dive into the research topics of 'Modelling systemic risk in the South African banking sector using CoVaR'. Together they form a unique fingerprint.

Cite this