Improved testing for the efficiency of asset pricing theories in linear factor models

This paper suggests a refinement of the standard T2 test statistic used in testing asset pricing theories in linear factor models. The test is designed to have improved power characteristics and to deal with the empirically important case where there are many more assets than time periods. This is necessary because the case of too few time periods invalidates the conventional T2. Furthermore, the test is shown to have reasonable power in cases where common factors are present in the residual covariance matrix

The effects of systematic sampling and temporal aggregation on discrete time long memory processes and their finite sample properties

This study investigates the effects of varying sampling intervals on the long memory characteristics of certain stochastic processes. We find that although different sampling intervals do not affect the decay rate of discrete time long memory autocorrelation functions in large lags, the autocorrelation functions in short lags are affected significantly. The level of the autocorrelation functions moves upward for temporally aggregated processes and downward for systematically sampled processes, and these effects result in a bias in the long memory parameter. For the ARFIMA(0,d,0) process, the absolute magnitude of the long memory parameter, |d|, of the temporally aggregated process is greater than the |d| of the true process, which is greater than the |d| of the systematically sampled process. We also find that the true long memory parameter can be obtained if we use a decay rate that is not affected by different sampling intervals

Forecasting Nonlinear Functions of Returns Using LINEX Loss Functions

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 function is particularly well-suited to many of these forecasting problems and can give better forecasts than conventional loss functions such as mean square error (MSE).LINEX Loss Function, Forecasting, Volatility

Smoothing, nonsynchronous appraisal and cross-sectional aggreagation in real estate price indices

participants at EFA, ERES, and AREUEA annual meetings for their helpful comments.

Using Bayesian variable selection methods to choose style factors in global stock return models

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 current asset returns. This study does not find compelling evidence for global styles as useful explanatory factors, once country and sector have been accounted for

The disappearance of style in the US equity market

This paper investigates the modelling of style returns in the US and the returns to style "tilts" based on forecasts of enhanced future style returns. We use hidden Markov model to build our forecasts. Our finding that style returns are less forecastible in more recent years is consistent with the hypothesis that style returns are the result of anomalies rather than risk premia. The erosion of anomalous returns as public awareness of their presence is translated into strategies that arbitrage away the excess returns seems to be a hypothesis consistent with our modelling results