Predictability Hidden by Anomalous Observations

62 Pages Posted: 23 Mar 2013 Last revised: 5 Mar 2014

See all articles by Lorenzo Camponovo

Lorenzo Camponovo

University of St. Gallen

O. Scaillet

University of Geneva GSEM and GFRI; Swiss Finance Institute; University of Geneva - Research Center for Statistics

Fabio Trojani

Swiss Finance Institute; University of Geneva

Date Written: March 3, 2014

Abstract

Testing procedures for predictive regressions with lagged autoregressive variables imply a suboptimal inference in presence of small violations of ideal assumptions. We propose a novel testing framework resistant to such violations, which is consistent with nearly integrated regressors and applicable to multi-predictor settings, when the data may only approximately follow a predictive regression model. The Monte Carlo evidence demonstrates large improvements of our approach, while the empirical analysis produces a strong robust evidence of market return predictability hidden by anomalous observations, both in- and out-of-sample, using predictive variables such as the dividend yield or the volatility risk premium.

Keywords: Predictive Regression, Stock Return Predictability, Bootstrap, Subsampling, Robustness

JEL Classification: C12, C13, G1

Suggested Citation

Camponovo, Lorenzo and Scaillet, Olivier and Trojani, Fabio, Predictability Hidden by Anomalous Observations (March 3, 2014). Swiss Finance Institute Research Paper No. 13-05, Available at SSRN: https://ssrn.com/abstract=2237447 or http://dx.doi.org/10.2139/ssrn.2237447

Lorenzo Camponovo

University of St. Gallen ( email )

Varnbuelstr. 14
Saint Gallen, St. Gallen CH-9000
Switzerland

Olivier Scaillet (Contact Author)

University of Geneva GSEM and GFRI ( email )

40 Boulevard du Pont d'Arve
Geneva 4, Geneva 1211
Switzerland
+ 41 22 379 88 16 (Phone)
+41 22 389 81 04 (Fax)

HOME PAGE: http://www.scaillet.ch

Swiss Finance Institute

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

University of Geneva - Research Center for Statistics

Geneva
Switzerland

Fabio Trojani

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

University of Geneva ( email )

Geneva, Geneva
Switzerland

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