A daily log-return can be regarded as a test statistic - specifically the (unscaled) sample mean of a sequence of intraday random variables. We discuss sufficient conditions for a dependent bootstrap to consistently and non-parametrically estimate the entire distribution of this “test...

We perform a large-scale empirical evaluation of Value-at-Risk forecasting models, including several that incorporate intraday data. Our analysis spans numerous equities on two distinct exchanges, covering the global financial crisis period as well as the more stable period that immediately...

The problem of ranking intra-day volatility estimators purely via empirical criteria is considered. Following the original work on this topic undertaken by Patton (2011a), the proposed methodology draws from the literature on loss-based forecast evaluation, but accounts for the inevitable...

In this paper, we apply the 7,846 technical trading rules considered by Sullivan et al. (1999) to a stock index, some individual stocks, some currencies and some interest rate futures contracts traded in the Australian financial markets. Size distortions due to data-snooping are avoided by using...

This study investigates the relationship between university quality and graduate starting salaries using pooled Australian data from the Graduate Destination survey and a two-stage estimation methodology. The results suggest that average starting salaries for young undergraduates differ...

We study whether Australian employers recognise immigrants' education acquired abroad, and if so how. Using data from the Longitudinal Surveys of Immigrants in Australia, we apply interval regression to model migrant hourly earnings. We find substantially higher returns from human capital...

Most studies of the returns to higher education ignore the effects of taxation on the distribution of the costs and benifits of education. In an analysis of hiogher education in Fiji, this study incorporates the tax sysyem in a model of the returns to education and finds that, rather than...