This paper studies the ICAPM intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly variance with past daily squared returns -- the Mixed Data Sampling (or MIDAS) approach. Using...

We show that decomposing macroeconomic risks across horizon is key to uncover a tight link between risk premia and the real economy. Exposure in four-year returns to innovations in macroeconomic growth and volatility with a matching half-life of over four years is priced in a wide variety of...

We find that commodity risk is priced in the cross-section of US stock returns. Following the financialization of commodities, investors hedge commodity price risk directly in the futures market, primarily via commodity index investments, whereas before they gained commodity exposure mainly via...

The risk-return trade-off implies that a riskier investment should demand a higher expected return relative to the risk-free return. The approach of Ghysels, Santa-Clara, and Valkanov (2005) consisted of estimating the risk-return trade-off with a mixed frequency, or MIDAS, approach. MIDAS...

We explore mixed data sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Volatility and related processes are our prime focus, though the regression method has wider applications in macroeconomics and finance, among other...

This paper studies the ICAPM intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly variance with past daily squared returns -- the Mixed Data Sampling (or MIDAS) approach. Using...