Abstract
This thesis aims to examine critically the extent of Zimbabwean banks’ compliance to cartesian (Basel II/III) and complexity science theories in Quantitative Risk Management. Traditionally, Quantitative Risk Management in banks is examined within the dichotomy of cartesianism and interpretivism where probability and subjective tools are applied respectively. However, the emergence of complexity science from natural and engineering sciences, offers a new perspective. Quantitative Risk Management in Zimbabwean banks is an unexplored field.
Mixed methods are employed to answer objectives of testing and examining critically the usefulness of cartesian and complexity science theories’ adoption in Zimbabwean banks. Data are collected from sixteen banks with structured questionnaires completed by 120 Risk Managers, and archival analysis on 112 annual audited financial statements and 20 reports from past surveys. Data analysis is done with descriptive statistics and hermeneutic methods for triangulation, complementarity, better evidence, and to obtain a holistic picture.
The study empirically discovers that to a large extent Zimbabwean banks have adopted Basel II/III and complexity science with the same speed and in the same direction. However, there is an unbalanced implementation of both theories due to information asymmetries. For instance, Basel II/III implementation reveals stronger compliance to calculative idealism (Pillar 1) than supervision (Pillar 2) and market discipline (Pillar 3). Similarly, complexity science shows stronger adoption of dynamic risk management framework than modeling methods. While all banks in Zimbabwe are compliant to Basel II/III capital modeling methods regardless of size thus creating a level plain field of competition, its usefulness in promoting financial stability is refuted because of diverging regulatory and economic capital. Furthermore, complexity science is found to be more useful where pattern-based management is a prerequisite.
This thesis contributes to knowledge in four specific ways. First, it provides empirical confirmation to laissez-faire theory that cartesian regulation is not useful in bank capitalisation. Second, it pioneers the application of complexity science theory from natural sciences to Quantitative Risk Management of banks within developing country settings. Third, it proposes a dynamic risk management framework and lastly, offers national policy recommendations to improve capital management in Zimbabwean banks.