Abstract
Given the availability of daily data over 1926-1962, it is surprising that there is no research examining the idiosyncratic volatility (IV) puzzle over this early period. This paper conducts an out-of-sample test on the IV phenomenon. We find that the negative relation between IV and expected returns only exists during the period 07/1963-12/1989, implying that the puzzle may be a result of data snooping bias. The result on time-special anomaly is robust for different sorting breakpoints and alternative measure of idiosyncratic volatility. Infrequent trading cannot account for the low average returns of stocks with high idiosyncratic volatility. With a striking contrast, the involving of short-term return reversals eliminates this dilemma.
Highlights
The systematic risk principle shows that firm-specific risk or idiosyncratic volatility (IV) should not carry a premium, whereas IV should positively predict return under Merton’s [1] incomplete-information model1 [2] [3] [4] [5]
All the results suggest that the idiosyncratic volatility puzzle is driven by the force of short-term return reversals and is a data snooping bias
We use data with a broader time horizon to examine some possible explanations for the idiosyncratic volatility puzzle
Summary
The systematic risk principle shows that firm-specific risk or idiosyncratic volatility (IV) should not carry a premium, whereas IV should positively predict return under Merton’s [1] incomplete-information model1 [2] [3] [4] [5]. We attempt to test whether the idiosyncratic volatility puzzle is a result of data snooping bias. The results of portfolios analysis suggest that the significant negative relation between IV and subsequent stock returns exists during 07/1963-12/2014. The results of portfolio returns suggest that the negative relationship between IV and expected returns is not alleviated when infrequent trading is considered during the prominent puzzle period 07/1963-12/1989. Perhaps most importantly, this paper provides evidence that the idiosyncratic volatility puzzle is a result of data snooping bias. We use the standard deviation of residuals from Fama-French five-factor model to measure idiosyncratic volatility.
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