OUT-OF-SAMPLE STOCK RETURN PREDICTION USING HIGHER-ORDER MOMENTS

Jose Faias and Tiago Castel-Branco ()
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Tiago Castel-Branco: Oxycapital, Av. Eng. Duarte Pacheco, Torre 2, 15 B, Lisbon, Portugal

International Journal of Theoretical and Applied Finance (IJTAF), 2018, vol. 21, issue 06, 1-27

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.

Keywords: Prediction; realized moments; implied moments; time-series; cross-section (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0219024918500437

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