New Testing Approaches for Mean-Variance Predictability

94 Pages Posted: 11 Jan 2019

See all articles by Gabriele Fiorentini

Gabriele Fiorentini

Universita di Firenze - Dipartimento di Statistica

Enrique Sentana

Centro de Estudios Monetarios y Financieros (CEMFI); Financial Markets Group; Centre for Economic Policy Research (CEPR)

Date Written: January 2019

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, Misspecification, Moment tests, robustness, volatility

JEL Classification: C12, C22, G17

Suggested Citation

Fiorentini, Gabriele and Sentana, Enrique, New Testing Approaches for Mean-Variance Predictability (January 2019). CEPR Discussion Paper No. DP13426. Available at SSRN: https://ssrn.com/abstract=3314044

Gabriele Fiorentini (Contact Author)

Universita di Firenze - Dipartimento di Statistica ( email )

Viale Morgagni, 59
50134 Firenze
Italy
+39 055 4237 274 (Phone)
+39 055 4223 560 (Fax)

Enrique Sentana

Centro de Estudios Monetarios y Financieros (CEMFI) ( email )

Casado del Alisal 5
28014 Madrid
Spain
+34 91 429 0551 (Phone)
+34 91 429 1056 (Fax)

HOME PAGE: http://www.cemfi.es/~sentana/

Financial Markets Group

Houghton Street
London School of Economics & Political Science (LSE)
London WC2A 2AE
United Kingdom
+44 20 7955 7002 (Phone)
+44 20 7852 3580 (Fax)

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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