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 quarterlyforecasting 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.