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The Empirical Risk-Return Relation: a factor analysis approach
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Abstract
Financial economists have long been interested in the empirical relation
between the conditional mean and conditional volatility of excess stock
market returns, often referred to as the risk-return relation.
Unfortunately, the body of empirical evidence on the risk-return relation is
mixed and inconclusive. A key criticism of the existing empirical literature
relates to the relatively small amount of conditioning information used to
model the conditional mean and conditional volatility of excess stock market
returns. To the extent that financial market participants have information
not reflected in the chosen conditioning variables, measures of conditional
mean and conditional volatility--and ultimately the risk-return relation
itself--will be misspecified and possibly highly misleading. We consider one
remedy to these problems using the methodology of dynamic factor analysis
for large datasets, whereby a large amount of economic information can be
summarized by a few estimated factors. We find that several estimated
factors contain important information about one-quarter ahead excess returns
and volatility that is not contained in commonly used predictor variables.
Moreover, the factor-augmented specifications we examine predict an unusual
16-20 percent of the one-quarter ahead variation in excess stock market
returns, and exhibit remarkably stable and strongly statistically
significant out-of-sample forecasting power. Finally, in contrast to several
pre-existing studies that rely on a small number of conditioning variables,
we find a positive conditional correlation between risk and return that is
strongly statistically significant, whereas the unconditional correlation is weakly negative and statistically snginficantpredictability, conditioning information, large dimension factor models