Analysing Financial Returns by Using Regression Models Based on Non-Symmetric Stable Distributions

The daily evolution of the price of Abbey National shares over a 10-week period is analysed by using regression models based on possibly non-symmetric stable distributions. These distributions, which are only known through their characteristic function, can be used in practice for interactive modelling of heavy-tailed processes. A regression model for the location parameter is proposed and shown to induce a similar model for the mode. Finally, regression models for the other three parameters of the stable distribution are introduced. The model found to fit best allows the skewness of the distribution, rather than the location or scale parameters, to vary over time. The most likely share return is thus changing over time although the region where most returns are observed is stationary.     

ANALYSIS OF INFLUENCE SHARING IN JUDGMENTAL FORECASTING: REGRESSION MODELS AND PERCEPTUAL MEASURE

The committee or group approach is often used where considerable uncertainties have to be handled by skillful judgment. In this paper the authors investigate the subject of influence sharing in judgmental forecasting of commodity prices. They propose a conceptual framework for patterns of influence sharing in groups and for influence roles of individuals. A statistical analysis of the influence of group members on the group forecast leads to inferences about influence pattern and roles in a group.     

METRIC VS. NONMETRIC PROCEDURES FOR MULTIATTRIBUTE MODELING: SOME SIMULATION RESULTS *

Nonmetric procedures such a MONANOVA have been developed to estimate attribute utilities with ranked observations. It is argued that the goodness of the estimation procedure is another criterion which favors a metric procedure like Ordinary Least Squares (OLS) Regression. MONANOVA and OLS Regression are compared in their ability to recover attribute utilities with both ranked and scaled observations.     

ON THE USE OF VERBAL PROTOCOLS IN CONJOINT ANALYSIS STUDIES

A recent study by Olshavsky and Acito helped gain insight into (a) the decision process used by respondents when asked to do a conjoint task, (b) the predictive validity of conjoint models compared to models derived from verbal protocols, and (c) the reasons for prediction errors. In this article, the author argues that the Olshavsky and Acito results do not lead one to believe that the compensatory conjoint models can be potentially more misleading than models that try to represent more accurately the underlying evaluation process with noncompensatory and compensatory rules. Hence, it does not seem necessary to try to identify noncompensatory rules in a conjoint study.