Working Paper: CEPR ID: DP13426
Authors: Gabriele Fiorentini; Enrique Sentana
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
JEL Codes: C12; C22; G17
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| nonnormality of financial returns (G17) | predictability of returns (G17) |
| semiparametric tests (C14) | causal mechanisms (D47) |
| regional factors (R11) | predictability of returns (G17) |