Browsing by Subject "Risk Management"

Now showing 1 - 5 of 5
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    Open Access
    A systemic exploration of information systems project risks in the South African public sector
    (2021) Chiloane, Poelo Leo; Tuan, Nien-Tsu
    Purpose: This study aims to investigate Information Systems (IS) project risks in the South African public sector, and to develop a systemic model of the most dominant risks encountered and identify the interrelationships that exist between these risks. Design and methodology: The study is conducted through the application of Interactive Management (IM) to identify IS project risks and structure the interrelationships between them. The IM methodology comprises of four key phases: Idea Generation, Idea Clarification, Idea Structuring, and Interpretation. A workshop with a group of participants is required to carry out an IM intervention successfully. During the Idea Generation phase, participants are asked a triggering question to elicit ideas, which are then clarified and structured in the subsequent phases of IM before final interpretation. Findings: In the Idea Generation phase, six IM participants working on public sector IS projects were asked a triggering question to elicit dominant IS project risks they perceive to be important. The participants initially identified 34 IS project risks, which were reduced to 24 after they brainstormed their relevance during the Idea Clarification phase. Further deliberations led to the participants removing another risk during the Idea Structuring phase. During the Idea Structuring phase, the remaining 23 risks were structured to produce an Interpretive Structural Modelling (ISM) digraph with the aid of software. The ISM digraph revealed three risk factors as the primary drivers of IS project risks in the public sector, specifically, in the context of this study. These risks are ‘lack of consultation with users', ‘budget cuts' and ‘excessive red tape'. Value of study: This research contributes to the following: (1) the existing knowledge-base on public sector IS project risk management; (2) the focus on a soft systemic approach such as IM helps in uncovering context-specific issues on IS project risks that may not be available in extant literature; and (3) the collaborative learning process of the IM approach adds to research on the sustainability of complex IS projects implemented in the public sector.
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    Open Access
    Assessing the effectiveness of risk management practices used by contractors in South African construction
    (2021) Chiswanda, Farai; Windapo, Abimbola
    This research examines the risk management practices prevalent in the South African construction industry. This was necessitated by the dearth in effective risk management in the construction industry particularly in developing countries such as South Africa. A comprehensive literature review was conducted to establish the risk management practices in use. Based on the literature, a questionnaire was developed and administered electronically to contractors operating in South Africa. The study established that contractors face a significant number of risks, chief among them, high competition in bids, political instability, payment delays, corruption and bribery and an overbearing influence of bureaucratic processes from government aligned agencies. Furthermore, it was also established that risk management amongst South African contractors is largely informal due to a mediocre appreciation of risk management. It also emerged that risk management implementation is perceived to be an expensive venture that erodes the marginal profits contractors aim to make. Resultantly, risk management practices implementation is low amongst the contractors. Based on the findings, the study concludes that the South African construction industry suffers from ineffective risk management implementation. To improve the implementation of risk management practices amongst contractors, it is recommended that contractors increase their risk management awareness through risk management training and risk knowledge management. Overall, this will be beneficial for their operations as risk management has been found to yield a positive effect on the meeting of project objectives. Furthermore, private and public sector clients are also encouraged to demand evidence of risk management competency from contractors upon engaging them for work.
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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
    Compound Lévy random bridges and credit risky asset pricing
    (2016) Ikpe, Dennis Chinemerem; Künzi, Hans-Peter A; Becker, Ronald; Mataramvura, Sure
    In this thesis, we study random bridges of a certain class of Lévy processes and their applications to credit risky asset pricing. In the first part, we construct the compound random bridges(CLRBs) and analyze some tools and properties that make them suitable models for information processes. We focus on the Markov property, dynamic consistency, measure changes and increment distributions. Thereafter, we consider applications in credit risky asset pricing. We generalize the information based credit risky asset pricing framework to incorporate prematurity default possibilities. Lastly we derive closed-form expressions for default trends and intensities for credit risky bonds with CLRB as the background partial information process. We obtain analytical expressions for specific CLRBs. The second part looks at application of stochastic filtering in the current information based asset pricing framework. First, we formulate credit risky asset pricing in the information-based framework as a filtering problem under incomplete information. We derive the Kalman-Bucy filter in one dimension for bridges of Lévy processes with a given finite variance.
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    Predicting corporate turnaround of listed companies in South Africa
    (2016) Chin, Chu-Kuo; West, Darron
    Corporate turnaround, in comparison to financial distress, is not substantially researched either internationally or locally in South Africa. This study attempts to explore this area of research by developing models that identify financially distressed companies with a potential for turnaround. This analysis examines listed companies on both the JSE Securities Exchange ('JSE') and Alternative Exchange ('AltX') for the period 2007 to 2014 by using available data from iNet BFA. The financial distress model, Taffler's Z-score, is used to identify companies that fall within the sample. Multiple linear discriminant models with interaction variables are used as part of the process to derive the turnaround models. The first model shows that efficiency is a key driver for a successful turnaround. The second model reveals that JSE-listed companies are more likely to survive than AltX companies. This study contributes to the existing research by identifying significant factors for corporate turnaround and summarizing its findings in a practical manner.