New testing approaches for mean-variance predictability

Enrique Sentana and Gabriele Fiorentini

No 13426, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We propose tests for smooth but persistent serial correlation in risk premia and volatilities that exploit the non-normality of financial returns. Our parametric tests are robust to distributional misspecification, while our semiparametric tests are as powerful as if we knew the true return distribution. Local power analyses confirm their gains over existing methods, while Monte Carlo exercises assess their finite sample reliability. We apply our tests to quarterly returns on the five Fama-French factors for international stocks, whose distributions are mostly symmetric and fat-tailed. Our results highlight noticeable differences across regions and factors and confirm the fragility of Gaussian tests.

Keywords: Financial forecasting; Moment tests; Misspecification; Robustness; Volatility (search for similar items in EconPapers)
JEL-codes: C12 C22 G17 (search for similar items in EconPapers)
Date: 2019-01
New Economics Papers: this item is included in nep-for, nep-ore and nep-rmg
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Related works:
Journal Article: New testing approaches for mean–variance predictability (2021)
Working Paper: New testing approaches for mean-variance predictability (2019)
Working Paper: New testing approaches for mean-variance predictability (2019)
Working Paper: New Testing Approaches for Mean-Variance Predictability (2018)
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