Improved Estimates of Correlation Coefficients and their Impact on Optimum Portfolios

Abstract

Abstract To implement mean variance analysis one needs a technique for forecasting correlation coefficients. In this article we investigate the ability of several techniques to forecast correlation coefficients between securities. We find that separately forecasting the average level of pair‐wise correlations and individual pair‐wise differences from the average improves forecasting accuracy. Furthermore, forming homogenous groups of firms on the basis of industry membership or firm attributes (e.g. size) improves forecast accuracy. Accuracy is evaluated in two ways: First, in terms of the error in estimating future correlation coefficients. Second, in the characteristics of portfolios formed on the basis of each forecasting technique. The ranking of forecasting techniques is robust across both methods of evaluation and the better techniques outperform prior suggestions in the literature of financial economics.

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