Quarterly beta forecasting: An evaluation

Abstract

Ever since the inception of betas as a measure of systematic risk, the forecast error in relation to this parameter has been a major concern to both academics and practitioners in finance. In order to reduce forecast error, this paper compares a series of competing models to forecast beta. Realized measures of asset return covariance and variance are computed and applied to forecast beta, following the advances in methodology of Andersen, Bollerslev, Diebold and Wu [Andersen, T. G., Bollerslev, T., Diebold, F. X., & Wu, J. (2005). A framework for exploring the macroeconomic determinants of systematic risk. American Economic Review, 95, 398–404; and Andersen, T. G., Bollerslev, T., Diebold, F. X., & Wu, J. (2006). Realized beta: Persistence and Predictability. In T. Fomby & D. Terrell (Eds.), Advances in Econometrics, vol 20B: Econometric Analysis of Economic and Financial Times Series., JAI Press, 1–40.]. This approach is compared with the constant beta model (the industry standard) and a variant, the random walk model. It is shown that an autoregressive model with two lags produces the lowest or close to the lowest error for quarterly stock beta forecasts. In general, the AR(2) model has a mean absolute forecast error half that of the constant beta model. This reduction in forecast error is a dramatic improvement over the benchmark constant model.