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Efficient Prediction of Excess Returns

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  • Jon Faust
  • Jonathan H. Wright

Abstract

It is well known that augmenting a standard linear regression model with variables that are correlated with the error term but uncorrelated with the original regressors will increase asymptotic efficiency of the original coefficients. We argue that in the context of predicting excess returns, valid augmenting variables exist and are likely to yield substantial gains in estimation efficiency and, hence, predictive accuracy. The proposed augmenting variables are ex post measures of an unforecastable component of excess returns: ex post errors from macroeconomic survey forecasts and the surprise components of asset price movements around macroeconomic news announcements. These "surprises" cannot be used directly in forecasting--they are not observed at the time that the forecast is made--but can nonetheless improve forecasting accuracy by reducing parameter estimation uncertainty. We derive formal results about the benefits and limits of this approach and apply it to standard examples of forecasting excess bond and equity returns. We find substantial improvements in out-of-sample forecast accuracy for standard excess bond return regressions; gains for forecasting excess stock returns are much smaller.

Suggested Citation

  • Jon Faust & Jonathan H. Wright, 2008. "Efficient Prediction of Excess Returns," NBER Working Papers 14169, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:14169
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    Cited by:

    1. Coen N. Teulings & Nikolay Zubanov, 2014. "Is Economic Recovery A Myth? Robust Estimation Of Impulse Responses," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 497-514, April.
    2. Erdemlioglu, Deniz, 2009. "Macro Factors in UK Excess Bond Returns: Principal Components and Factor-Model Approach," MPRA Paper 28895, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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