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

Market risk and the concept of fundamental volatility : measuring volatility across asset and derivative markets and testing for the impact of derivatives markets on financial markets

This paper proposes an unobserved fundamental component of volatility as a measure of risk. This concept of fundamental volatility may be more meaningful than the usual measures of volatility for market regulators. Fundamental volatility can be obtained using a stochastic volatility model, which allows us to ‘filter’ out the signal in the volatility information. We decompose four FTSE100 stock index related volatilities into transitory noise and unobserved fundamental volatility. Our analysis is applied to the question as to whether derivative markets destabilise asset markets. We find that introducing European options reduces fundamental volatility, while transitory noise in the underlying and futures markets does not show significant changes. We conclude that, for the FTSE100 index, introducing a new options market has stabilised both the underlying market and existing derivative markets

The asset allocation decision in a loss aversion world

The purpose of this paper is to derive explicit formulae for the asset allocation decision for the loss aversion utility function proposed by Kahneman and Tuversky. We show that these utility functions exhibit constant absolute risk aversion. We also give analytic results which interpret the assumptions of risk-aversion with respect to gains but risk-a!ection with respect to losses in terms of changes of the optimal investment of equity when the probability that equity outperforms cash goes up. For the Knight, Satchell and Tran (1995) family of distributions, it is straightforward to derive closed form expressions for the optimal portfolio weights in all cases. Using UK and US data, we confirmed that the values of the parameters in the loss aversion function suggested by many previous studies are compatible with the observed proportions held in equity in both the UK and the US. The distributional assumptions are not innocuous. However, whilst modelling upside and downside returns by gamma distributions leads to plausible results, modelling upside and downside by truncated normals does not