Table of Contents
Econometrics, Volume 8, Issue 2 (June 2020) – 15 articles
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Cover Story (view full-size image) We investigate the marginal predictive content of small versus large jump variation. In our [...] Read more. We investigate the marginal predictive content of small versus large jump variation. In our portfolios, sorting on signed small jump variation leads to greater value-weighted return differentials between stocks than when either signed total jump or signed large jump variation is sorted on. The benefit of signed small jump variation investing is driven by stock selection within an industry, rather than industry bets. However, signed jump variation has stronger predictive power than both upside and downside jump variation. One reason large and small jump variation have differing marginal predictive content is that the predictive content of signed large jump variation is negligible when controlling for either signed total jump variation or realized skewness. By contrast, signed small jump variation has unique information for predicting future returns. View this paper