Predictability Hidden by Anomalous Observations

Lorenzo Camponovo, Olivier Scaillet and Fabio Trojani
Additional contact information
Lorenzo Camponovo: University of St. Gallen
Fabio Trojani: Swiss Finance Institute; University of Geneva

No 13-05, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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 (search for similar items in EconPapers)
JEL-codes: C12 C13 G1 (search for similar items in EconPapers)
Pages: 62 pages
Date: 2013-03
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Citations: View citations in EconPapers (7)

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Related works:
Working Paper: Predictability Hidden by Anomalous Observations (2018)
Working Paper: Predictability Hidden by Anomalous Observations (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp1305

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