The Relationship between Regulation and Solvency Risk for the Top Five South African Banks


  • Sani Nasa Tafara North-West University
  • Daniel Mokatsanyane North-West University
  • Zandri Dickason-Koekemoer North-West University


z-score; Auto-Regressive Distributed Lag model; logistic regression


Objectives: the objectives of this paper were to analyse different types of risks that banks face, conduct in-depth analysis of the various types of bank regulatory measures, and determine whether there is a long run or short-run relationship between bank solvency and the implementation of bank regulation Prior Work this research builds up on studies about bank risk and regulation but include an African aspect Approach Secondary data from banks’ financial statements was used to calculate solvency risk using a z-score model. The results of the z-score model represented solvency risk and performed as the dependent variable in the study. Results Logit Regression showed that the z-score for South African banks cannot be used to predict whether new regulation will be implemented in the future. However, an ARDL model indicated that there is a long-run relationship between the z-score for the top five South African banks and new regulation being implemented. Implications This study can be used by academic researchers as a comparison to their own academic work Value Based on the varying results from the different methodologies implemented in this study, it can be recommended that more regulation needs to be implemented that specifically looks into increasing the solvency levels of South African banks.


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How to Cite

Tafara, . S. N., Mokatsanyane, D. ., & Dickason-Koekemoer, Z. (2020). The Relationship between Regulation and Solvency Risk for the Top Five South African Banks: Array. Acta Universitatis Danubius. Œconomica, 16(6). Retrieved from



Financial Economics