Out-of-sample stock return prediction using higher-order moments

José Afonso Faias*, Tiago Castel-Branco

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

We analyze variance, skewness and kurtosis risk premia and their option-implied and realized components as predictors of excess market returns and of the cross-section of stock returns. We find that the variance risk premium is the only moment-based variable to predict S&P 500 index excess returns, with a monthly out-of-sample R2 above 6% for the period between 2001 and 2014. Nonetheless, all aggregate moment-based variables are effective in predicting the cross-section of stock returns. Self-financed portfolios long on the stocks least exposed to the aggregate moment-based variable and short on the stocks most exposed to it achieve positive and significant Carhart 4-factor alphas and a considerably higher Sharpe ratio than the S&P 500 index, with positive skewness.
Original languageEnglish
Article number1850043
JournalInternational Journal of Theoretical and Applied Finance
Volume21
Issue number6
DOIs
Publication statusPublished - 1 Sept 2018

Keywords

  • Cross-section
  • Implied moments
  • Prediction
  • Realized moments
  • Time-series

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