Submitted by Kym Marcel Martins Ardison (kymmarcel@gmail.com) on 2015-04-06T19:04:20Z No. of bitstreams: 1 Tail Risk - Original.pdf: 817189 bytes, checksum: 02561a6a7cb94d1480a4f78933486df4 (MD5) / Approved for entry into archive by BRUNA BARROS (bruna.barros@fgv.br) on 2015-04-28T12:21:10Z (GMT) No. of bitstreams: 1 Tail Risk - Original.pdf: 817189 bytes, checksum: 02561a6a7cb94d1480a4f78933486df4 (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2015-05-04T12:33:49Z (GMT) No. of bitstreams: 1 Tail Risk - Original.pdf: 817189 bytes, checksum: 02561a6a7cb94d1480a4f78933486df4 (MD5) / Made available in DSpace on 2015-05-04T12:37:02Z (GMT). No. of bitstreams: 1 Tail Risk - Original.pdf: 817189 bytes, checksum: 02561a6a7cb94d1480a4f78933486df4 (MD5) 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.