This paper investigates the question whether option-implied information may enhance the estimation quality of the Capital Asset Pricing Model and its extensions. Following the approach of Buss and Vilkov (2011), we calculate implied stock correlations for the S\&P100 constituents and include these into the Factor model estimations. Even though the models based on risk-neutral inputs show a slightly better performance, we cannot report any distinctive risk-return relation. Explanatory power remains weak for the models, with the exception of the Beta prediction themselves. The analysis across two subperiods reveal distinctive cycle effects. Almost all models perform better after the outset of the financial crisis, which opens the door for in further research in this direction.

Implied correlation and variance risk premium stand out in predicting market returns. However, while the predictive ability of implied correlation lasts for up to a year, the variance risk premium predicts market returns only for one quarter ahead. Contrary to the accepted view, implied correlation predicts the market return not through a diversification risk (average correlation) channel, but by predicting a concentration of market exposure, which defines the level of non-diversifiable market risk, or systematic diversification. Economy-wide implied correlation built exclusively from option prices of nine sector ETFs and the S&P500 efficiently predicts future market returns and systematic diversification risk in the form of market betas dispersion. Newly developed implied correlations for economic sectors provide industry-related information and are used to extract option-implied risk factors from sector-based covariances.

Motivated by extensive evidence that stock-return correlations are stochastic, we analyze whether the risk of correlation changes (affecting diversification benefits) is priced. We propose a direct and intuitive test by comparing option-implied correlations between stock returns (obtained by combining index option prices with prices of options on all index components) with realized correlations. Our parsimonious model shows that the substantial gap between average implied (39.5% for S&P500 and 46.0% for DJ30) and realized correlations (32.5% and 35.5%, respectively) is direct evidence of a large negative correlation risk premium. Empirical implementation of our model also indicates that the index variance risk premium can be attributed to the high price of correlation risk. Finally, we provide evidence that option-implied correlations have remarkable predictive power for future market returns, which also stays significant after controlling for a number of fundamental market return predictors.

We introduce a new tensor derived computation of higher dimensional average implied correlations determined from high frequency options panels. Using supersymmetric identities to decrease we can determine average moments and co-moments for equities and incorporate these forward looking measures to a standard asset pricing framework. To compute the metrics we use both a broad market index (S&P 500) and nine sector indices versus the underlying components. Our empirical analysis shows that the cross section stock returns have substantial exposure to risk captured by the market average correlation factor. The results are robust to various permutations of the estimation procedure. The risk premium of the market average correlation factor is statistically and economically significant when controlling the other common market risk factors or firm characteristics. Finally, we test the higher-order CAPM with the ex ante market beta, gamma, and theta approximated by the option-implied average correlations. In line with the evidence documented in previous literature, we find positive significant risk premiums for the ex ante market beta and theta but mixed results for the ex ante market gamma.

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