We investigate two methods for using daily stock returns to forecast, and update forecasts of, quarterly real output growth. Both methods aggregate daily returns in some manner to form a single stock market variable. We consider (i) augmenting the quarterly AR(1) model for real output growth with daily returns using a nonparametric Mixed Data Sampling (MIDAS) setting, and (ii) augmenting the quarterly AR(1) model with the most recent r -day returns as an additional predictor. We discover that adding low frequency stock returns (up to annual returns, depending on forecast horizon) to a quarterly AR(1) model improves forecasts of output growth
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Publisher Info
Paper provided by Singapore Management University, School of Economics in its series Working Papers with number
14-2007.
Length: 29 pages Date of creation: Mar 2007 Date of revision: Publication status: Published in SMU Economics and Statistics Working Paper Series Handle: RePEc:siu:wpaper:14-2007