A Model-Free Measure of Aggregate Idiosyncratic Volatility and the Prediction of Market Returns
2014 ◽
Vol 49
(5-6)
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pp. 1133-1165
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AbstractIn this paper, we formally show that the cross-sectional variance of stock returns is a consistent and asymptotically efficient estimator for aggregate idiosyncratic volatility. This measure has two key advantages: It is model free and observable at any frequency. Previous approaches have used monthly model-based measures constructed from time series of daily returns. The newly proposed cross-sectional volatility measure is a strong predictor for future returns on the aggregate stock market at the daily frequency. Using the cross section of size and book-to-market portfolios, we show that the portfolios’ exposures to the aggregate idiosyncratic volatility risk predict the cross section of expected returns.
2008 ◽
Vol 43
(1)
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pp. 29-58
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2017 ◽
Vol 25
(4)
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pp. 509-545
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