Predictability Hidden by Anomalous Observations

Lorenzo Camponovo, Olivier Scaillet and Fabio Trojani ()

Papers from arXiv.org

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.

Date: 2016-12
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (9)

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http://arxiv.org/pdf/1612.05072 Latest version (application/pdf)

Related works:
Working Paper: Predictability Hidden by Anomalous Observations (2018)
Working Paper: Predictability Hidden by Anomalous Observations (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1612.05072

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