Certain models of the term structure of interest rates exhibit unspanned stochastic volatility (USV). A model has this property if it involves a source of stochastic variation — called an unspanned factor — that does not affect the model’s interest rates directly, but does affect the extent to which future interests are liable to change (that is, interest-rate volatility). This thesis is concerned with these models, from a variety of perspectives. Firstly, the theoretical foundation of the USV property is addressed. Formal definitions of unspanned factors and USV are developed, generalising ones tentatively proposed in the literature. Several results from these definitions and the accompanying framework are derived. Particularly, the ability to hedge general claims (i.e., the completeness or lack thereof) of these models is examined in detail. Examples are given to illustrate the features of the proposed framework and the necessity of the generalised definitions. Secondly, the empirical issue of whether USV models are necessary to plausibly represent observed interest-rate markets is interrogated. An empirical derivative-hedging approach is adopted, the results of which are contextualised by also treating data simulated from models with USV and non-USV versions. It is shown that hedging effectiveness is relatively robust to the presence of USV, which resolves the apparent conflict between the two studies that have taken a hedging approach to this question. Despite the cross-sectional hedging effects being surprisingly minor, further regression results show that USV models are needed to model the time series of market interest rates. Finally, the thesis addresses a certain class of models that exhibit USV: those with one spanned factor (driving interest-rate variation) and one unspanned, volatility-related factor. Being the simplest non-trivial USV models, these bivariate USV models are fundamental, and — like onefactor models in general settings — are helpful in introducing and comparing higher-factor models when simple ones are insufficient. These models are shown to exist (contradicting a claim in the literature); to share a particular affine form for their bond pricing functions; and to necessarily exhibit a short-term interest rate with dynamics of a certain type. A specific bivariate USV model is then proposed, which is analysed and compared to others in the literature.

Estimating and extrapolating long term equity implied volatilities is of importance in the investment and insurance industry, where ’long term’ refers to periods of ten to thirty years. Market-consistent calibration is difficult to perform in the South African market due to lack of long term liquid tradable derivatives. In this case, practitioners have to estimate the implied volatility surface across a range of expiries and moneyness levels. A detailed evaluation is performed for different estimation techniques to assess the strengths and weaknesses of each of the models. The estimation techniques considered include statistical and time-series techniques, non-parametric techniques and three potential methods which use the local volatility model.

SA has one of the lowest survival rates of small and medium enterprises (hereafter referred to as “SMEs”), in the world (Edmore, December 2011). Therefore, business rescue is critical in developing SA’s economy, as defined in Section 7(b)(i) of the Companies Act, No.71 of 2008 (“the Act”) which reads: “Promote the development of the South African Economy by encouraging entrepreneurship and enterprise efficieny” The literature on business rescue concludes that post commencement finance is critical to the success of business rescue. However, to date, there has been no research performed on actual data collected from practitioners to answer the question of whether post commencement finance is a predictor of a successful business rescue The findings of this study initially contradict the literature insofar as 56% of business rescues received post commencement finance: however, further investigation showed that only 7% of the total companies in this study received third party financial institutional post commencement finance, with the balance being introduced by shareholders. The main finding of this study was that the introduction of post commencement finance is only a partial predictor of a successful business rescue. Thus, in the case of those companies which received finance, under business rescue, only 57% were successful. Another finding of this study is that the combination that provides the best probability of successful business rescue is when equity, in the business rescue company, is made available after the successful adoption of the business rescue plan.

This thesis explores three important issues in credit risk modeling: the nonlinear credit risk stress testing models, the recovery term structure of point-in-time (PIT) loss given default (LGD), and the estimation of LGD by mixture beta regression model. In the first essay of this thesis, we study the credit risk stress testing models. By incorporating the regime-switching and quantile regression techniques into credit risk stress testing models, we propose two new dynamic models that outperform the traditional linear regression model according to both the point estimate accuracy and the confidence interval breaches. This confirms the importance of nonlinear regression approaches in the estimation and the prediction of credit risk determinants. The proposed models perform especially well in capturing the extreme values on the tail of the estimated distribution of the credit risk measure. The proposed models could be used for both the International Financial Reporting Standard 9 (IFRS9) expected loss calculation and Basel’s Advanced Internal Rating-Based (AIRB) regulatory capital calculation purposes. In the second essay, we examine and model the time-series pattern of recovery throughout the bankruptcy and workout process of a retail credit portfolio; whereas other researchers are concerned with predicting the overall recovery rates of debt instruments, we model the amounts a creditor can recover at different points in time subsequent to the default event. This is of practical interest to commercial banks in managing the risk of their default loan portfolios. Like managing performing loan portfolios, banks must assign loss provision and determine the capital requirement associated with non-performing (i.e., defaulted) loan portfolios. Given the fact that it usually takes two to three (up to five or more) years to complete the recovery process for a typical defaulted retail (corporate) loan, it is important to understand the time-varying risk characteristic of the defaulted portfolio as a function of its vintage in the recovery process. An accurate point-in-time (PIT) risk assessment enables financial institutions to manage their defaulted loan portfolios in a timely fashion. In the third essay, we further extend our understanding of the distribution of LGD. For credit risk management purposes, the LGD of credit instruments is one of the key determinants in estimating capital requirements for financial institutions. To address the practical problems encountered in implementing LGD prediction model (e.g., in capturing the bimodal characteristic of the LGD distribution), we propose to develop a mixture beta regression LGD model. By using the maximum likelihood estimation and the method of moment approaches, the parameters of the mixture beta regression model can be estimated. Furthermore, we examine the impact of the systematic factors and model the time-series variation of the LGD distribution as a function of these systematic factors. Finally, through a number of empirical analyses, we demonstrate the superior performance of our proposed mixture beta models in comparison with the single-beta logit-linked model commonly considered in the literature. / Thesis / Doctor of Philosophy (PhD)

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While sleep loss has been shown to have detrimental effects on cognitive, physiological, and psychological processes, it has only recently been investigated as a possible causal factor of risk-taking behavior (i.e., a conscious choice to engage in dangerous behavior despite knowledge of possible loss or harm). Among the few studies that have been conducted in this field, the majority found that as individuals become sleepier, their propensity to engage in risk-taking behavior increased. The results of the current study indicated a positive relationship between increased sleep loss and two measures of specific risk-taking behavior (i.e., substance use, sexual compulsivity), but no significant relationship between sleep loss and measures of general risk-taking behavior. There was some evidence for temporal stability of the Iowa Gambling Task (IGT), though scores on the IGT were not related to scores on other measures of risk-taking, nor to measures of sleep loss. Negative mood was found to partially mediate the relationship between sleep loss and substance use, as well as the relationship between sleep loss and sexual compulsivity.

M.Comm. (Financial Economics) / Enterprise risk management (ERM) is emerging as a risk management methodology that is seemingly superior to that of traditional, silo-based risk management. Although ERM implementation is on the increase, research into ERM is still limited. There is, for instance, a lack of clarity within the literature regarding which factors lead to companies embracing ERM, as well as a lack of consensus on ERM’s benefits. The purpose of this study was therefore to explore the drivers of ERM implementation, its inhibitors and enablers, the benefits that are realised through ERM, as well as the advantages and disadvantages associated with ERM as a risk management methodology. Data were gathered through semi-structured, face-to-face interviews with seven risk practitioners working in the South African mining industry. The study found that drivers of ERM implementation include regulatory pressure and compliance with corporate governance and listing requirements, but that there are other incentives. Inhibitors of ERM implementation include the large amount of managerial time needed, competition with other initiatives, resistance, and low initial buy-in levels, as well as a shortage of experienced ERM practitioners. Regarding ERM enablers, the design of the ERM framework is seen as critical, as is sound project discipline in planning and organising the implementation, along with visible support from executive and senior management, and ongoing training. Benefits derived through ERM include greater confidence that the company has a complete understanding of its risk profile, better decision-making, and improved tracking of risk mitigation. Disadvantages associated with ERM include the tendency of it being regarded as a corporate administrative function, subjectivity, and difficulty in aligning ERM to short- and medium-term priorities, as compared to longer-term strategic issues. This study makes a unique contribution to the existing body of knowledge on ERM by exploring the disadvantages associated with ERM as a risk management methodology. At a practical level and with reference to the South African mining industry, in particular, this study provides more clarity on the rationale for adopting ERM, as well as the challenges associated with implementing and sustaining ERM programmes. Recommendations are made with respect to ERM in practice, as well as for further research on ERM.

University of Technology, Sydney. Faculty of Engineering. / In today’s business environment, increased competition, market globalisation, increased customer demands and accelerated technologies require organisations to focus on efficiency in every aspect of their operations. Many studies in operations management have focused on the improvement of operational performance, including reduction of process variability, increasing flexibility or implementing controls in operations. However, managing the risk in operations seems to have been neglected by researchers. Hence, there are two major objectives of this study. The first objective is to investigate the use of the operational risk management (ORM) systems in Australia and study the factors that have an impact on effective operational risk management. Then, based on the identified factors, the second objective is to develop an ORM system implementation model and guideline for Australian organisations. A review of the ORM systems and its implementation was conducted. As a result of this investigation, a definition of ORM system in this study was formulated and the factors of effective ORM system implementation were identified as a basis for the next stage of this study. An investigation of the factors of ORM system implementation was then carried out. An extensive questionnaire survey was used to collect empirical data from Australian organisations. Statistical analysis results and feedback from experts was used to develop an applicable model and guideline for ORM system implementation. The main outcome of this study is a proposed model and guideline for ORM system implementation in Australian organisations, which will assist the organisation to manage operational risks more effectively and provide motivation for carrying out further research in ORM.

Thesis (Ph. D.)--Dept. of Economics and Dept. of Mathematics. University of Missouri--Kansas City, 2006. / "A dissertation in economics and mathematics." Advisor: James I. Sturgeon. Typescript. Vita. Title from "catalog record" of the print edition Description based on contents viewed Dec. 19, 2007. Includes bibliographical references (leaves 191-203). Online version of the print edition.