Approximately forty percent of youths’ waking hours are unrestricted and not committed to activities such as eating, sleeping or going to school. Many of this free time is spent without companionship or supervision from adults, which puts them at risk of spending their time out on the streets, where the risk of succumbing to peer pressure and becoming involved in inappropriate or illegal activities is increased. The absence of structured activities, stimulation and support can lead to youth becoming involved in rebellious and unwanted behaviour, partly due to their continuous search for adventure and excitement. Learners with Mentally Mild Learning Disabilities (MMLD) can be seen as youth at risk because of their academic and behavioural problems. Learning disabilities can increase the risk factors for delinquency and substance dependence. Previous research suggests that adventure-based experiential programmes (AEPs), which are highly structured, can thus be a very powerful intervention or prevention medium to empower youth at risk to overcome obstacles through the acquisition and practise of skills. The purpose of the study was firstly to determine what the personal functioning profile of MMLD youth looks like, which was done in order to gain insight into the different aspects of the personal functioning, which were focused on when developing the AEP. The study was secondly done to determine what the role of an AEP is on the personal functioning of learners with MMLD. Books, journals, dissertations, theses and internet sources were used to do a thorough literature review. The literature review was done in order for readers to understand the link between MMLD youth and an AEP. The literature review gave an introduction to the phenomenon of MMLD youth and also explained how an AEP could be beneficial towards them. A qualitative research design was used by the researcher in the form of an instrumental case study. Case studies make it possible for the researcher to use qualitative as well as quantitative constructs for data gathering purposes. Sampling of participants was done in two steps. A school for Learners with Special Education Needs (LSEN) (Die Wilge High School) was sampled through purposeful sampling by means of criterion-based sampling techniques. The second step was to sample fourteen male learners from Die Wilge High School through purposive sampling. The personal functioning was determined by using the Youth at Risk Assessment Scale (YAR3) Questionnaire 3. The researcher made use of semi-structured, one-on-one interviews and field notes in order to gather data. The field notes and transcribed interviews were analysed in order to obtain a clear picture of the content, which was then used to identify the codes. After the data was analysed four main themes, each with its own categories, were identified. The themes related to the personal functioning of the youth and consisted of interpersonal relationships, trust, self-worth and perseverance. The learners had positive feedback regarding their interpersonal relationships, trust, self-worth and perseverance before participation in the AEP. This was attributed to the Strengths-based Approach which the researcher followed in the development of the AEP. Despite this positive feedback, the learners still felt that there was an improvement after the AEP in all of the areas. Most of the learners attributed the improvement to learning more about each other, learning to work together and building friendships among each other. These new found friendships made it easier to trust each other and believe in themselves. The learners also realised that they experienced more positive feelings from persevering than when they quit. The results were used to discuss the objective of the study and to determine if the researcher met the objective. / MA (Recreational Science), North-West University, Potchefstroom Campus, 2015

Approximately forty percent of youths’ waking hours are unrestricted and not committed to activities such as eating, sleeping or going to school. Many of this free time is spent without companionship or supervision from adults, which puts them at risk of spending their time out on the streets, where the risk of succumbing to peer pressure and becoming involved in inappropriate or illegal activities is increased. The absence of structured activities, stimulation and support can lead to youth becoming involved in rebellious and unwanted behaviour, partly due to their continuous search for adventure and excitement. Learners with Mentally Mild Learning Disabilities (MMLD) can be seen as youth at risk because of their academic and behavioural problems. Learning disabilities can increase the risk factors for delinquency and substance dependence. Previous research suggests that adventure-based experiential programmes (AEPs), which are highly structured, can thus be a very powerful intervention or prevention medium to empower youth at risk to overcome obstacles through the acquisition and practise of skills. The purpose of the study was firstly to determine what the personal functioning profile of MMLD youth looks like, which was done in order to gain insight into the different aspects of the personal functioning, which were focused on when developing the AEP. The study was secondly done to determine what the role of an AEP is on the personal functioning of learners with MMLD. Books, journals, dissertations, theses and internet sources were used to do a thorough literature review. The literature review was done in order for readers to understand the link between MMLD youth and an AEP. The literature review gave an introduction to the phenomenon of MMLD youth and also explained how an AEP could be beneficial towards them. A qualitative research design was used by the researcher in the form of an instrumental case study. Case studies make it possible for the researcher to use qualitative as well as quantitative constructs for data gathering purposes. Sampling of participants was done in two steps. A school for Learners with Special Education Needs (LSEN) (Die Wilge High School) was sampled through purposeful sampling by means of criterion-based sampling techniques. The second step was to sample fourteen male learners from Die Wilge High School through purposive sampling. The personal functioning was determined by using the Youth at Risk Assessment Scale (YAR3) Questionnaire 3. The researcher made use of semi-structured, one-on-one interviews and field notes in order to gather data. The field notes and transcribed interviews were analysed in order to obtain a clear picture of the content, which was then used to identify the codes. After the data was analysed four main themes, each with its own categories, were identified. The themes related to the personal functioning of the youth and consisted of interpersonal relationships, trust, self-worth and perseverance. The learners had positive feedback regarding their interpersonal relationships, trust, self-worth and perseverance before participation in the AEP. This was attributed to the Strengths-based Approach which the researcher followed in the development of the AEP. Despite this positive feedback, the learners still felt that there was an improvement after the AEP in all of the areas. Most of the learners attributed the improvement to learning more about each other, learning to work together and building friendships among each other. These new found friendships made it easier to trust each other and believe in themselves. The learners also realised that they experienced more positive feelings from persevering than when they quit. The results were used to discuss the objective of the study and to determine if the researcher met the objective. / MA (Recreational Science), North-West University, Potchefstroom Campus, 2015

This dissertation consists of two chapters. The first chapter establishes an algorithm for calculating capital requirements. The calculation of capital requirements for financial institutions usually entails a reevaluation of the company's assets and liabilities at some future point in time for a (large) number of stochastic forecasts of economic and firm-specific variables. The complexity of this nested valuation problem leads many companies to struggle with the implementation. The current chapter proposes and analyzes a novel approach to this computational problem based on least-squares regression and Monte Carlo simulations. Our approach is motivated by a well-known method for pricing non-European derivatives. We study convergence of the algorithm and analyze the resulting estimate for practically important risk measures. Moreover, we address the problem of how to choose the regressors, and show that an optimal choice is given by the left singular functions of the corresponding valuation operator. Our numerical examples demonstrate that the algorithm can produce accurate results at relatively low computational costs, particularly when relying on the optimal basis functions. The second chapter discusses another application of regression-based methods, in the context of pricing variable annuities. Advanced life insurance products with exercise-dependent financial guarantees present challenging problems in view of pricing and risk management. In particular, due to the complexity of the guarantees and since practical valuation frameworks include a variety of stochastic risk factors, conventional methods that are based on the discretization of the underlying (Markov) state space may not be feasible. As a practical alternative, this chapter explores the applicability of Least-Squares Monte Carlo (LSM) methods familiar from American option pricing in this context. Unlike previous literature we consider optionality beyond surrendering the contract, where we focus on popular withdrawal benefits - so-called GMWBs - within Variable Annuities. We introduce different LSM variants, particularly the regression-now and regression-later approaches, and explore their viability and potential pitfalls. We commence our numerical analysis in a basic Black-Scholes framework, where we compare the LSM results to those from a discretization approach. We then extend the model to include various relevant risk factors and compare the results to those from the basic framework.

We compare the traditional GARCH models with a semiparametric approach based on extreme value theory and find that the semiparametric approach yields more accurate predictions of Value-at-Risk (VaR). Using traditional parametric approaches based on GARCH and EGARCH to model the conditional volatility, we calculate univariate one-day ahead predictions of Value-at-Risk (VaR) under varying distributional assumptions. The accuracy of these predictions is then compared to that of a semiparametric approach, based on results from extreme value theory. For the 95% VaR, the EGARCH’s ability to incorporate the asymmetric behaviour of return volatility proves most useful. For higher quantiles, however, we show that what matters most for predictive accuracy is the underlying distributional assumption of the innovations, where the normal distribution falls behind other distributions which allow for thicker tails. Both the semiparametric approach and the conditional volatility models based on the t-distribution outperform the normal, especially at higher quantiles. As for the comparison between the semiparametric approach and the conditional volatility models with t-distributed innovations, the results are mixed. However, the evidence indicates that there certainly is a place for extreme value theory in financial risk management.

Academic coaching has demonstrated positive relationships with college students' academic engagement and performance. A university campus in Puerto Rico implemented academic coaching for at-risk students, but the program has not been studied for its impact on student engagement. Guided by self-regulation theory and constructivism, this quasi-experimental study examined differences in engagement and identification of best teaching behaviors between students who experienced academic coaching (n = 115) and those who did not (n = 55). Students completed the Classroom Survey of Student Engagement (CLASSE) before and after the 4-week instructional unit and the Instructor Behavior Checklist (IBC) after the instructional unit. The data from the CLASSE and IBC were analyzed using mixed analysis of variance for engagement activities and student identification of effective teaching practices. There were no significant findings relating academic coaching to engagement; however, the experimental group identified significantly more best teaching practices used by their instructor. A Pearson correlation also yielded a significant positive relationship between students' engagement and the identification of instructor best practices. Based on these findings, a professional development program was created for instructors, which fosters student engagement and learning by encouraging instructor best practices through a classroom coaching model. The findings from this study may promote positive social change by helping to prepare faculty to integrate academic coaching and best teaching practices related to student engagement.

AbstractStudents come to community colleges with different levels of personal development, academic preparedness, and learning needs. Success programs that focus on the holistic development of nontraditional students provide an important pathway into college for students who might not otherwise attend or succeed. These programs face increased accountability to demonstrate student outcomes. In assessing outcomes, are the successes experienced by these students fully captured with traditional student success measures?Constituent groups may differ with regard to expected outcomes and conceptualizations of success. To examine this possibility, a community college program designed to promote goal attainment for at-risk, nontraditional re-entry students was chosen for a case study to determine what success means to the students who participate in the program, the instructional counselors who teach the course for the program, and the administrators who make resource allocation decisions that impact the viability of the program.The case study was organized around four propositions that hypothesize how different participants construct their perceptions of success:1) Students who complete the program course will come to search for and define success in terms of finding their voice and developing cultural capital;2) Instructors who teach the course will conceive of success outcomes in differing ways depending on the extent of their professionalization - locals will support a more traditional, academic oriented preparation whereas cosmopolitans and intermediates, to varying degrees, will embrace a more holistically developmental approach to the course;3) Administrators will evaluate and allocate resources to the program primarily in terms of traditional institutional measures of student success - student credit production and student completion; and3a) Perspectives of success based on students finding their voice, cultural capital, and holistic developmental outcomes are not considered nor valued independently by administrators in their decision-making.Knowing the differing perspectives of what is valued by those involved allows for strategically informed decisions about what to assess and how to present data that best supports the benefits of this program to the students, the college, and the community. The importance of aligning various participant perspectives of success for ultimate program efficiency and effectiveness is demonstrated.

This thesis compares insurance premium principles with current financial risk paradigms and uses distorted probabilities, a recent development in premium principle literature, to synthesize the current models for financial risk measures in banking and insurance. This work attempts to broaden the definition of value-at-risk beyond the percentile measures. Examples are used to show how the percentile measure fails to give consistent results, and how it can be manipulated. A new class of consistent risk measures is investigated.

We consider the research problem of quantitative support for decision making in supply chain network design (SCND). We first identify the requirements for a comprehensive SCND as (i) a methodology to select uncertainties, (ii) a stochastic optimisation model, and (iii) an appropriate solution algorithm. We propose a process to select a manageable number of uncertainties to be included in a stochastic program for SCND. We develop a comprehensive two-stage stochastic program for SCND that includes uncertainty in demand, currency exchange rates, labour costs, productivity, supplier costs, and transport costs. Also, we consider conditional value at risk (CV@R) to explore the trade-off between risk and return. We use a scenario generator based on moment matching to represent the multivariate uncertainty. The resulting stochastic integer program is computationally challenging and we propose a novel iterative solution algorithm called adaptive scenario refinement (ASR) to process the problem. We describe the rationale underlying ASR, validate it for a set of benchmark problems, and discuss the benefits of the algorithm applied to our SCND problem. Finally, we demonstrate the benefits of the proposed model in a case study and show that multiple sources of uncertainty and risk are important to consider in the SCND. Whereas in the literature most research is on demand uncertainty, our study suggests that exchange rate uncertainty is more important for the choice of optimal supply chain strategies in international production networks. The SCND model and the use of the coherent downside risk measure in the stochastic program are innovative and novel; these and the ASR solution algorithm taken together make contributions to knowledge.

The growth of the financial risk management industry has been motivated by the increased volatility of financial markets combined with the rapid innovation of derivatives. Since the 1970s, several financial crises have occurred globally with devastating consequences for financial and non-financial institutions and for the real economy. The most recent US subprime crisis led to enormous losses for financial and non-financial institutions and to a recession in many countries including the US and UK. A common lesson from these crises is that advanced financial risk management systems are required. Financial risk management is a continuous process of identifying, modeling, forecasting and monitoring risk exposures arising from financial investments. The Value at Risk (VaR) methodology has served as one of the most important tools used in this process. This quantitative tool, which was first invented by JPMorgan in its Risk-Metrics system in 1995, has undergone a considerable revolution and development during the last 15 years. It has now become one of the most prominent tools employed by financial institutions, regulators, asset managers and nonfinancial corporations for risk measurement. My PhD research undertakes a comprehensive and practical study of market risk modeling in modern finance using the VaR methodology. Two newly developed risk models are proposed in this research, which are derived by integrating volatility modeling and the quantile regression technique. Compared to the existing risk models, these two new models place more emphasis on dynamic risk adjustment. The empirical results on both real and simulated data shows that under certain circumstances, the risk prediction generated from these models is more accurate and efficient in capturing time varying risk evolution than traditional risk measures. Academically, the aim of this research is to make some improvements and extensions of the existing market risk modeling techniques. In practice, the purpose of this research is to support risk managers developing a dynamic market risk measurement system, which will function well for different market states and asset categories. The system can be used by financial institutions and non-financial institutions for either passive risk measurement or active risk control.

Bitcoin is an unregulated digital currency originally introduced in 2008 without legal tender status. Based on a decentralized peer-to-peer network to confirm transactions and generate a limited amount of new bitcoins, it functions without the backing of a central bank or any other monitoring authority. In recent years, Bitcoin has seen increasing media coverage and trading volume, as well as major capital gains and losses in a high volatility environment. Interestingly, an analysis of Bitcoin returns shows remarkably low correlations with traditional investment assets such as other currencies, stocks, bonds or commodities such as gold or oil. In this paper, we shed light on the impact an investment in Bitcoin can have on an already well-diversified investment portfolio. Due to the non-normal nature of Bitcoin returns, we do not propose the classic mean-variance approach, but adopt a Conditional Value-at-Risk framework that does not require asset returns to be normally distributed. Our results indicate that Bitcoin should be included in optimal portfolios. Even though an investment in Bitcoin increases the CVaR of a portfolio, this additional risk is overcompensated by high returns leading to better return-risk ratios.