Background: Procyclicality plays a pivotal role in finance in both thriving and crisis periods. This influence stems not only from the way market participants behave but also from risk metrics used and regulatory capital amassed and released during bust and boom periods, respectively. The introduction of the regulatory Countercyclical Capital Buffer aims to thwart procyclicality by accumulating (releasing) capital in upswings (downswings), subsequently reducing the amplitude of the financial cycle and promoting macroprudential stability. The timing of the accumulation and release of buffer capital is critical so identifying accurate indicators is important. Aim: This paper applies a Kalman filter to South African data and confirms the procyclicality of the Basel Committee on Banking Supervision (BCBS) proposal. Setting: For South Africa, studies suggest alternatives such as residential property indices because research has demonstrated that the BCBS proposal is procyclical rather than countercyclical. Methods: This paper applies a Kalman filter to South African data and compares the results obtained with those filtered using the Hodrick–Prescott filter. Results: Results indicate that buffer signals are dependent upon the filter employed. Conclusion: Buffer signals are strongly dependent upon the filter employed to detect procyclicality. The South African Reserve Bank and other regulators should reconsider the use of the Hodrick–Prescott filter and entertain the possibility of using the Kalman filter instead.
A stress-testing model to evaluate liquidity and systemic risk in banks of developed and emerging economies has been assembled and tested. The Liquidity Stress Tester model (LST) was applied to Dutch and UK markets during crisis and non-crisis periods in previous research – here it is applied to South African banks. The flexibility and adaptability of the LST allows different banking systems and reactions of system participants to be evaluated comprehensively. Feedback effects arising from bank reactions to severely stressed haircuts and increases in systemic risk caused by reputation degradation are considered, as is the effect of enhanced contagion from other banks.
Background: Tradeable credit assets are vulnerable to two varieties of credit risk: default risk (which manifests itself as a binary outcome) and spread risk (which arises as spreads change continuously). Current (2017) regulatory credit risk rules require banks to hold capital for both these risks. Aggregating these capital amounts is non-trivial. Aim: The aim was to implement the bubble value at risk (buVaR) approach, proposed by Wong (2011) to overcome the risk aggregation problem. This method accounts for diversification and for procyclicality and operates by inflating the positive side of the underlying return distribution, in direct proportion to prevailing credit spread levels (usually liquid credit default swap spreads). Setting: The principal setting for the study was the South African credit market which represents a developing market. Previous work by Wong (2011) focussed only on developed markets. Methods: Using South African data, closed form solutions were derived for free parameters of Wong’s formulation, and the relationship between the spread level and the response function was developed and calibrated. Results: The results indicate that the original calibrations and assumptions made by Wong (2011) would result in excessive capital requirement for South African banks. Estimates obtained from this work suggest further calibration is required to cover the unique features of the South African milieu. Considerable differences compared with other markets were also found. Conclusion: The application of buVaR to South African government bond credit default swaps spreads highlighted the metric’s countercyclical properties that would potentially have countered bubble developments had they been implemented during the credit crisis of 2008/2009. Regulatory authorities should take this important metric into account when allocating South African bank’s credit risk capital.
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