Estimating the Risk-Return Trade-off with Overlapping Data Inference
Asset pricing models such as the conditional CAPM are typically estimated with MLE using a monthly or quarterly horizon with data sampled to match the horizon even though daily data are available. We develop an overlapping data inference methodology (ODIN) that uses all of the data while maintaining the monthly or quarterly forecasting period, and we apply it to the conditional CAPM. Our approach recognizes that the first order conditions of MLE can be used as orthogonality conditions of GMM. Using historical data, we find considerable differences in the estimates from the non-overlapping samples that begin on different days.
We thank Jules van Binsbergen, Eric Ghysels, Christian Lundblad, Alberto Plazzi, Rossen Valkanov, and the seminar participants at the Stanford Graduate School of Business for helpful comments; and we thank Norman White for research computing assistance. This research was supported by a grant from the Network for Study on Pensions, Aging, and Retirement to the Columbia Business School. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Esben Hedegaard & Robert J. Hodrick, 2016. "Estimating the risk-return trade-off with overlapping data inference," Journal of Banking & Finance, vol 67, pages 135-145. citation courtesy of