Market Beta Dynamics and Portfolio Efficiency

Eric Ghysels
University of North Carolina

Eric Jacquier

This paper introduces a new  estimation for the dynamics of  betas. It combines two previously separate approaches in the literature, data-driven filters and parametric methods. Namely, we show how to estimate the parametric beta dynamics by instrumental variables combined with block-sampling - but not overlapping window filters - of data-driven betas. Instrumental variables are needed because of the measurement errors in empirical betas. We find that, while betas are very strongly autocorrelated, neither aggregate nor firm-specific variables explain much of their quarterly variation. We then compare block-samplers and overlapping window filters using a criterion of economic significance. Namely,  we track the out-of-sample performance of portfolios optimized subject to target beta constraints. For target betas of zero, the case of many hedge funds, we show that estimation error results in systematic overshooting of the target beta. These portfolios benefit from the use of  medium to  long  term estimation windows of daily returns.