This paper applies LINEX loss functions to forecasting nonlinear functions of variance. We derive the optimal one-step-ahead LINEX forecast for various volatility models using data transformations such as ln(y2t) where yt is the return of the asset. Our results suggest that the LINEX loss...

This article investigates the modelling of style returns in the United States and the returns to style 'tilts' based on forecasts of enhanced future style returns. We use hidden Markov model to build our forecasts for data from 1975 to 1998. We do not include more recent observations as the...

This paper applies Bayesian variable selection methods from the statistics literature to give guidance in the decision to include/omit factors in a global (linear factor) stock return model. Once one has accounted for country and sector, it is possible to see which style or styles best explains...

In this paper, we propose the average <italic>F</italic>-statistic for testing linear asset pricing models. The average pricing error, captured in the statistic, is of more interest than the <italic>ex post</italic> maximum pricing error of the multivariate <italic>F</italic>-statistic that is associated with extreme long and short positions and...

It is shown that the ML estimates of the popular GARCH(1,1) model are significantly negatively biased in small samples and that in many cases converged estimates are not possible with Bollerslev's non-negativity conditions. Results also indicate that a high level of persistence in GARCH(1,1)...

This study introduces GARCH models with cross-sectional market volatility, which are called GARCHX models. The cross-sectional market volatility is a special case of common heteroscedasticity in asset specific returns, which is suggested by Connor and Linton (2001) as an important component in...