Browsing by Author "Gross, Eden"

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    Banking regulation: a Bayesian network approach to risk management
    (2025) Gross, Eden; Kruger, Ryan; Toerien, Francois
    The ever-evolving regulation surrounding banks and market risk, coupled with increased computing power, make for favourable conditions in employing machine learning techniques to estimate and forecast market risk metrics such as value at risk (VaR) and expected shortfall (ES). This study consists of three sections. First, this study comprehensively examines the performance of various market risk models when producing VaR and ES, and their stressed counterparts, using Standard and Poor's (S&P) 500 index returns from 1991 to 2020. The initial results show that autoregressive models are the most accurate of the traditional market risk models. Second, the first section's results are then used as the basis against which a novel and comprehensive Bayesian network (BN) methodology for producing VaR and ES forecasts, and those of their stressed counterparts, is assessed in the context of banking regulations, using four learning algorithms. The forecasts generated by the BNs are not found to offer any improved accuracy when incorporated into the market risk metric calculations, primarily due to the limited weight of the forecast in the return distribution relative to the historical returns in the return probability density function. Finally, a novel integrated forecast dynamic Bayesian network (IFDBN) methodology is developed, whereby, for each metric, the best-in-class autoregressive model and the best-in-class BN learning algorithm are coupled to produce market risk forecasts. The results of the IFDBNs are mixed, with the stressed ES metric IFDBN being the only IFDBN to produce more accurate forecasts relative to its traditional autoregressive counterpart. While certain market risk metrics may benefit from using IFDBNs in the forecasting process, this result is not universal, and the risk practitioner must evaluate the usefulness of IFDBNs on a case-by-case basis.
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    Open Access
    Banking regulation: a bayesian network approach to risk management
    (2025) Gross, Eden; Kruger, Ryan; Toerien, Francois
    The ever-evolving regulation surrounding banks and market risk, coupled with increased computing power, make for favourable conditions in employing machine learning techniques to estimate and forecast market risk metrics such as value at risk (VaR) and expected shortfall (ES). This study consists of three sections. First, this study comprehensively examines the performance of various market risk models when producing VaR and ES, and their stressed counterparts, using Standard and Poor's (S&P) 5 00 index returns from 1991 to 2020. The initial results show that autoregressive models are the most accurate of the traditional market risk models. Second, the first section's results are then used as the basis against which a novel and comprehensive Bayesian network (BN) methodology for producing VaR and ES forecasts, and those of their stressed counterparts, is assessed in the context of banking regulations, using four learning algorithms. The forecasts generated by the BNs are not found to offer any improved accuracy when incorporated into the market risk metric calculations, primarily due to the limited weight of the forecast in the return distribution relative to the historical returns in the return probability density function. Finally, a novel integrated forecast dynamic Bayesian network (IFDBN) methodology is developed, whereby, for each metric, the best -in-class autoregressive model and the best-in-class BN learning algorithm are coupled to produce market risk forecasts. The results of the IFDBNs are mixed, with the stressed ES metric IFDBN being the only IFDBN to produce more accurate forecasts relative to its traditional autoregressive counterpart. While certain market risk metrics may benefit from using IFDBNs in the forecasting process, this result is not universal, and the risk practitioner must evaluate the usefulness of IFDBNs on a case-by-case basis.
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    Open Access
    Risk Management in South Africa Before, During, and After the 2008 Global Financial Crisis: An Application to Different Sectors
    (2020) Gross, Eden; Kruger, Ryan
    The risk management functions of most financial institutions occupy themselves with the estimation of the value at risk (VaR) of their portfolios as a measure of market risk. Various methods are available to calculate the VaR measure, and this can be done at various degrees of confidence. This study evaluates and analyses the performance of five popular VaR forecasting methods in the South African context, using the closing values of three of the major indices available on the Johannesburg Stock Exchange (JSE), namely the All Share Index (ALSI), the Financials-Industrials Index (FINDI), and the Resources Index (RESI). These three indices are considered based on the findings of prior studies that indicate that not only does decomposing the ALSI into its constituent (the FINDI and the RESI) indices provide a better measurement of market risk on the JSE, but these sub-indices also have different systematic risk exposures which may necessitate different treatments in measuring their risks appropriately. The periods examined surrounded the 2008 global financial crisis in order to allow an evaluation of the impact of varying levels of volatility on the analysis. Overall, the study concludes that the performance of the VaR models examined is similar when assessing the risk of the ALSI and the RESI returns, while they are very different for the FINDI. This conclusion provides crucial insight into the risk management and investment decisions concerning portfolios which are more heavily invested in the FINDI as opposed to the other two, as this study suggests that a blanket treatment to the South African market is incorrect.