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Nonparametric tail risk, macroeconomics and stock returns: predictability and risk premia

Submitted by Kym Marcel Martins Ardison (kymmarcel@gmail.com) on 2015-04-06T19:04:20Z
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Previous issue date: 2015-02-12 / This paper proposes a new novel to calculate tail risks incorporating risk-neutral information without dependence on options data. Proceeding via a non parametric approach we derive a stochastic discount factor that correctly price a chosen panel of stocks returns. With the assumption that states probabilities are homogeneous we back out the risk neutral distribution and calculate five primitive tail risk measures, all extracted from this risk neutral probability. The final measure is than set as the first principal component of the preliminary measures. Using six Fama-French size and book to market portfolios to calculate our tail risk, we find that it has significant predictive power when forecasting market returns one month ahead, aggregate U.S. consumption and GDP one quarter ahead and also macroeconomic activity indexes. Conditional Fama-Macbeth two-pass cross-sectional regressions reveal that our factor present a positive risk premium when controlling for traditional factors.

Identiferoai:union.ndltd.org:IBICT/oai:bibliotecadigital.fgv.br:10438/13666
Date12 February 2015
CreatorsArdison, Kym Marcel Martins
ContributorsCosta, Carlos Eugênio da, Vicente, José, Escolas::EPGE, FGV, Almeida, Caio Ibsen Rodrigues de
Source SetsIBICT Brazilian ETDs
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis
Sourcereponame:Repositório Institucional do FGV, instname:Fundação Getulio Vargas, instacron:FGV
Rightsinfo:eu-repo/semantics/openAccess

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