Evaluating multivariate volatility forecasts : how effective are statistical and economic loss functions?

Multivariate volatility forecasts are an important input in many financial applications, in particular portfolio optimisation problems. Given the number of models available and the range of loss functions to discriminate between them, it is obvious that selecting the optimal forecasting model is challenging. The aim of this thesis is to thoroughly investigate how effective many commonly used statistical (MSE and QLIKE) and economic (portfolio variance and portfolio utility) loss functions are at discriminating between competing multivariate volatility forecasts. An analytical investigation of the loss functions is performed to determine whether they identify the correct forecast as the best forecast. This is followed by an extensive simulation study examines the ability of the loss functions to consistently rank forecasts, and their statistical power within tests of predictive ability. For the tests of predictive ability, the model confidence set (MCS) approach of Hansen, Lunde and Nason (2003, 2011) is employed. As well, an empirical study investigates whether simulation findings hold in a realistic setting. In light of these earlier studies, a major empirical study seeks to identify the set of superior multivariate volatility forecasting models from 43 models that use either daily squared returns or realised volatility to generate forecasts. This study also assesses how the choice of volatility proxy affects the ability of the statistical loss functions to discriminate between forecasts. Analysis of the loss functions shows that QLIKE, MSE and portfolio variance can discriminate between multivariate volatility forecasts, while portfolio utility cannot. An examination of the effective loss functions shows that they all can identify the correct forecast at a point in time, however, their ability to discriminate between competing forecasts does vary. That is, QLIKE is identified as the most effective loss function, followed by portfolio variance which is then followed by MSE. The major empirical analysis reports that the optimal set of multivariate volatility forecasting models includes forecasts generated from daily squared returns and realised volatility. Furthermore, it finds that the volatility proxy affects the statistical loss functions’ ability to discriminate between forecasts in tests of predictive ability. These findings deepen our understanding of how to choose between competing multivariate volatility forecasts.

Conceptions of school-based youth health nursing

The aim of this research is to explore the meaning of the experience of school-based youth health nursing in Queensland, Australia. The research follows a qualitative approach and is based on indepth interviews. The dominant experience is negative because participants feel they have to battle to gain respect and survive in the school environment. The small, positive experience of school-based youth health nursing is related to student consultations. Student consultations are a ‘golden egg’ because participants gain a sense of reward from making a difference to student wellbeing. This paper proposes operational recommendations including those related to health promotion and professional development and strategic recommendations regarding this model of school nursing. The authors conclude, first, that this ‘golden egg’ should be promoted to ensure all school nurses reap the rewards, second, that this model of school nursing is not the most effective model.

Designing university, government and industry community engagement to build informed and effective interventions in the area of road safety

Individuals, community organisations and industry have always been involved to varying degrees in efforts to address the Queensland road toll. Traditionally, road crash prevention efforts have been led by state and local government organisations. While community and industry groups have sometimes become involved (e.g. Driver Reviver campaign), their efforts have largely been uncoordinated and under-resourced. A common strength of these initiatives lies in the energy, enthusiasm and persistence of community-based efforts. Conversely, a weakness has sometimes been the lack of knowledge, awareness or prioritisation of evidence-based interventions or their capacity to build on collaborative efforts. In 2000, the Queensland University of Technology’s Centre for Accident Research and Road Safety – Queensland (CARRS-Q) identified this issue as an opportunity to bridge practice and research and began acknowledging a selection of these initiatives, in partnership with the RACQ, through the Queensland Road Safety Awards program. After nine years it became apparent there was need to strengthen this connection, with the Centre establishing a Community Engagement Workshop in 2009 as part of the overall Awards program. With an aim of providing community participants opportunities to see, hear and discuss the experiences of others, this event was further developed in 2010, and with the collaboration of the Queensland Department of Transport and Main Roads, the RACQ, Queensland Police Service and Leighton Contractors Pty Ltd, a stand-alone Queensland Road Safety Awards Community Engagement Workshop was held in 2010. Each collaborating organisation recognised a need to mobilise the community through effective information and knowledge sharing, and recognised that learning and discussion can influence lasting behaviour change and action in this often emotive, yet not always evidence-based, area. This free event featured a number of speakers representing successful projects from around Australia and overseas. Attendees were encouraged to interact with the speakers, to ask questions, and most importantly, build connections with other attendees to build a ‘community road safety army’ all working throughout Australia on projects underpinned by evaluated research. The workshop facilitated the integration of research, policy and grass-roots action enhancing the success of community road safety initiatives. For collaboration partners, the event enabled them to transfer their knowledge in an engaged approach, working within a more personal communication process. An analysis of the success factors for this event identified openness to community groups and individuals, relevance of content to local initiatives, generous support with the provision of online materials and ongoing communication with key staff members as critical and supports the view that the university can directly provide both the leadership and the research needed for effective and credible community-based initiatives to address injury and death on the roads.

A theory of waste behaviour in the construction industry

Levels of waste within the construction industry need to be reduced for environmental and economic reasons. Changing people's wasteful behaviour can make a significant contribution. This paper describes a research project that used Ajzen's 'theory of planned behaviour' to investigate the attitudinal forces that shape behaviour at the operative level. It concludes that operatives see waste as an inevitable by-product of construction activity. Attitudes towards waste management are not negative, although they are pragmatic and impeded by perceptions of a lack of managerial commitment. Waste management is perceived as a low project priority, and there is an absence of appropriate resources and incentives to support it. A theory of waste behaviour is proposed for the construction industry, and recommendations are made to help managers improve operatives' attitudes towards waste.