Browsing by Subject "VaR"

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    Open Access
    An Analysis of the Low-Volatility Anomaly on the Johannesburg Stock Exchange
    (2019) Harrisberg, Richard; Huang, Chun-Sung
    The low-volatility anomaly can be described as the unexpected outperformance of low-volatility stocks compared to high-volatility stocks over the long-term. This dissertation investigates the low-volatility anomaly and its presence on the Johannesburg Stock Exchange (JSE). Possible reasons behind why low-volatility stocks consistently outperform their high volatility counterparts, as well as their own expected return, over the long-term are discussed. This includes analysing how financial risk is measured and whether this plays a role in obscuring the expected risk-return relationship, in addition to other fundamental factors impacting expected returns. It is found that the low-volatility anomaly is present on the JSE and that using a number of different risk metrics does not significantly change where a stock is ranked on the risk spectrum. Additionally, including an interest rate exposure factor, a value factor and a momentum factor lowers the unexpected portion (Alpha) of the returns of low volatility stocks, at the same time as narrowing the gap between the unexpected performance of the lowest and highest volatility stocks.
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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) 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.