Chris Heaton

Benefit–Cost analysis of foreign student flows from developing countries: The case of postgraduate education

Abstract This paper demonstrates the formulation and computation of major items of benefit and cost that might be included in an evaluation of social rates of return to foreign study, and considers the question of incidence of measured effects between sending, host and third countries. Interest is focused especially on the South/North flow of students at postgraduate level. The methods discussed are illustrated through an application to the case of postgraduate students from Fiji studying at Australian universities. Internal rates of return and net incidence outcomes are calculated, and some policy implications are explored. [ JEL I21]

What does high-dimensional factor analysis tell us about risk factors in the Australian stock market?

Estimates of the cost of equity are often sensitive to the specification of the linear factor model used in their construction. In this article, we use techniques developed for high-dimensional factor models to consider the identity of systematic risk factors in the Australian equities market. Our results support the use of neither the Capital Asset Pricing Model (CAPM) nor the Fama and French model, although they provide an explanation for the empirical performance of these models. Many other model specifications are also rejected. We find that a single-factor model with an equal-weighted market index is the best model for estimating the cost of equity in the Australian context.

The Contribution of Structural Shocks to Australian Unemployment

In this paper dynamic factor analysis techniques are used to decompose changes in unemployment into industry sectoral and common components. Sectoral shocks are important, but the dominant causes of variation in unemployment are common to all industries. This is particularly the case for low-frequency fluctuations in unemployment. The pattern of the estimated sectoral shocks reflects the well-documented shift of employment from agriculture and manufacturing to services, and we find no evidence that microeconomic reform has contributed greatly to unemployment. Copyright 2002 by The Economic Society of Australia.

International Commodity Prices and the Australian Stock Market.

We propose a method for estimating the earliest time during the trading day when overnight information is reflected in domestic share prices, and use it to measure the impact of international commodities on four Australian Securities Exchange (ASX) indices. While evidence is found that the ASX opening price does not fully reflect overnight news, this information is absorbed within 15 min of the opening time. Using appropriately constructed returns, we find international commodities to have a statistically significant and economically meaningful effect on the ASX. Nevertheless, the S&P 500 index appears to be a more important contributor of relevant overnight information.

Bootstrapping Daily Returns

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 statistic”, up to, but not including, the location parameter. The method proposed is robust to market microstructure effects. There are many possible applications. In this paper, two are considered: 1) Estimating daily variance, and 2) Estimating Value-at-Risk (VaR). Of particular import: the VaR estimator is combined with the framework described in Patton & Li (2013) to produce forecast evaluation tests that are significantly more powerful than current popular techniques.

Estimation of high-dimensional linear factor models with grouped variables

We introduce a generalization of the approximate factor model that divides the observable variables into groups, allows for arbitrarily strong cross-correlation between the disturbance terms of variables that belong to the same group, and for weak correlation between the disturbances of variables that belong to different groups. We call this model the Grouped Variable Approximate Factor Model. We establish identification, propose an estimation approach based on instrumental variable conditions that hold in the limit, and prove consistency in a dual limit framework. Monte Carlo simulations are used to investigate the performance of the estimator, and the techniques are applied to an analysis of industrial output in the US.

An Empirical Analysis of Value-at-Risk Forecasting Models

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 followed.Studies of this nature have been performed before, although generally not on such a large scale. The main appeal of the present paper is two-fold: first, we utilize recent advances in econometric theory to ensure our forecast evaluation procedure is much more powerful than procedures in other comparable studies, and second, we introduce a new class of Value-at-Risk forecasting models that employ an intraday-based proxy. Our analysis leads to several recommendations regarding the choice of Value-at-Risk forecast models. Of particular interest: we find strong evidence to support the use of intraday data.