Forecasting the return and risk on a portfolio of assets
Portfolio selection algorithms use estimates of expected return and risk as their primary input. It is axiomatic that the accuracy of this input has a major impact on the quality of the final selection decision. This paper examines the forecasting performance of the models used to provide these estimates of return and risk. The approach used looks at both pricing models and time series models. Pricing models describe the inter-relationships between returns on different assets and the associated risk. Time series models describe the evolution of returns and risk over time. A random portfolio approach is used to test the forecasting performance of the combinations of each type of model, in terms of expected return and risk. The choice of models is shown to be critical and the relative performance of the models is described.