Predictability Hidden by Anomalous Observations

Lorenzo Camponovo, Olivier Scaillet and Fabio Trojani
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Lorenzo Camponovo: University of Surrey
Fabio Trojani: University of Geneva and Swiss Finance Institute

No 418, School of Economics Discussion Papers from School of Economics, University of Surrey

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.

JEL-codes: C12 C13 G1 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2018-02
New Economics Papers: this item is included in nep-ore
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https://repec.som.surrey.ac.uk/2018/DP04-18.pdf (application/pdf)

Related works:
Working Paper: Predictability Hidden by Anomalous Observations (2016)
Working Paper: Predictability Hidden by Anomalous Observations (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:sur:surrec:0418

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