Alone against the crowd: Individual differences in referees' ability to cope under pressure

This paper contributes to the recent debate about the role of referees in the home advantage phenomenon. Specifically, it aims to provide a convincing answer to the newly posed question of the existence of individual differences among referees in terms of the home advantage (Boyko, Boyko, & Boyko, 2007; Johnston, 2008). Using multilevel modelling on a large and representative dataset we find that (1) the home advantage effect differs significantly among referees, and (2) this relationship is moderated by the size of the crowd. These new results suggest that a part of the home advantage is due to the effect of the crowd on the referees, and that some referees are more prone to be influenced by the crowd than others. This provides strong evidence to indicate that referees are a significant contributing factor to the home advantage. The implications of these findings are discussed both in terms of the relevant social psychological research, and with respect to the selection, assessment, and training of referees.

Collaborative inquiries into literacy, place and identity in changing policy contexts : implications for teacher development

In this chapter we describe a history of collaboration between university-based literacy researchers and school-based teachers in teacher development programs and practitioner inquiries designed to improve literacy outcomes for students living in low-socioeconomic circumstances. We consider how an inquiry stance has informed teachers working for social justice through curriculum and pedagogy designed to connect children’s developing literacy repertoires with their changing material, social and linguistic contexts. We use examples from the practices of two of our long-term teacher-collaborators to show what has been possible to achieve, even in radically different policy contexts, because of teachers’ continued commitment to themes of place and belonging, and language and identity.

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.