New Testing Approaches for Mean-Variance Predictability

Gabriele Fiorentini ^[1] ; Enrique Sentana ^[2]
1. [1] University of Florence
  
  University of Florence
  
  Firenze, Italia
2. [2] CEMFI
Localización: Documentos de Trabajo ( CEMFI ), Nº. 14 (CEMFI Working Paper No. 1814, December 2018), 2018
Idioma: inglés
Enlaces
- Texto completo (pdf)

Resumen
- 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.