Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87040
Authors: 
Cakmakli, Cem
van Dijk, Dick
Year of Publication: 
2010
Series/Report no.: 
Tinbergen Institute Discussion Paper 10-115/4
Abstract: 
This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only include valuation ratios and interest rate related variables, and possibly individual macro variables, as well as the historical average excess return. The improvements in out-of-sample forecast accuracy are both statistically and economically significant. The factor-augmented predictive regressions have superior market timing ability and volatility timing ability, while a mean-variance investor would be willing to pay an annual performance fee of several hundreds of basis points to switch from the predictions offered by the benchmark models to those of the factor-augmented models. An important reason for the superior performance of the factor-augmented predictive regressions is the stability of their forecast accuracy, whereas the benchmark models suffer from a forecast breakdown during the 1990s.
Subjects: 
return predictability
model uncertainty
dynamic factor models
variable selection
JEL: 
C22
C53
G11
G12
Document Type: 
Working Paper

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