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In this study the martingale hypothesis concerning the stock index futures market is analyzed. The purpose is to understand how this notion concerning the behavior of the index futures affects the forecasting process. In addition, the forecasting of both daily and weekly stock index futures is examined. For daily forecasting, we find that the martingale method outperforms stepwise autoregressive and exponential smoothing methods However, for weekly forecasts, the stepwise autoregressive method is best.

The different types of estimators of rational expectations models are surveyed. A key feature is that the model′s solution has to be taken into account when it is estimated. The two ways of doing this, the substitution and errors‐in‐variables methods, give rise to different estimators. In the former case, a generalised least‐squares or maximum‐likelihood type estimator generally gives consistent and efficient estimates. In the latter case, a generalised instrumental variable (GIV) type estimator is needed. Because the substitution method involves more complicated restrictions and because it resolves the solution indeterminacy in a more arbitary fashion, when there are forward‐looking expectations, the errors‐in‐variables solution with the GIV estimator is the recommended combination.

This paper empirically investigates the link between expected returns on stocks and a set of variables that describe the general state of economic activity. The model relates the first and second conditional moments on stock excess returns to the conditional variances and covariances of a set of prespecified macroeconomic factors. The estimation results suggest that industrial production growth, inflation, and short‐term interest rates help explain the behavior over time of expected excess returns on stocks.

Standard multivariate tests of mean variance efficiency (MVE) have been criticised on the grounds that they require regression residuals to have a multivariate normal distribution. Generally, the existing evidence suggests that the normality assumption is questionable, even for monthly returns. MacKinlay and Richardson (1991) developed a generalised method of moments (GMM) framework which provides tests which are valid under much weaker distributional assumptions. They examined monthly US data formed into size based portfolios, for mean‐variance efficiency relative to the Sharpe‐Lintner CAPM. They found that inferences regarding mean‐variance efficiency can be sensitive to the test considered. In this paper we further investigate their GMM tests using monthly Australian data over the period 1974 to 1994. We extend upon their analysis to consider an alternative version of their GMM test and also to examine a zero‐beta version of the CAPM. Similar to the US case, our results also indicate sensitivity of inferences to the tests used. Finally, while we find that the GMM tests generally provide rejection of mean‐variance efficiency, tests involving the zero‐beta CAPM, particularly when a value‐weighted market index is used, prove less prone to rejection.

In this paper we employ a GMM‐based approach to test the restrictions imposed by a two‐factor ‘market and oil’ pricing model when a risk‐free asset is assumed to exist. We examine the Australian market which has several interesting features including self‐sufficiency in relation to oil, a large concentration of natural resource companies, susceptibility to the ‘Dutch disease’ and a diverse industry base. We extend previous literature by examining industry sector equity returns as different industry groups are likely to have different exposures to an oil factor, particularly in Australia. In the formal tests, we find evidence in favour of the model, particularly for industrial sector industries. The preferred model includes a domestic portfolio proxy for market returns in addition to the oil price factor and we find evidence of a positive market risk premium as well as a significantly priced oil factor.