Market Risk Factors
Saturday, Jan. 7, 2017 7:30 PM – 9:30 PM
- Chair: Ralph Koijen, London Business School
Dividend Risk Premia
AbstractThis paper studies time variation in expected excess returns of traded claims on dividends, bonds, and stock indices for US and international markets. We construct dividend risk factors corresponding to the well-known bond risk factors of Cochrane and Piazzesi (2005) and run predictive regressions of one-year annual excess returns on both risk factors. We find both return forecasting factors to be important for the prediction of returns on stock indices and traded dividends, but only the bond risk factor is highly relevant for bond returns. Further analyzing the components of risk, we find global factors to explain the largest part of the variation in index excess returns, while local factors still improve the fit for bond and dividend markets. The return-forecasting factors also predict excess returns for regions and assets that we did not use to construct the risk factors (equity indices in developed and emerging markets, emerging market bonds, corporate bond indices and a volatility selling strategy), suggesting substantial comovement in international risk premia.
Low Risk Anomalies?
AbstractThis paper shows theoretically and empirically that beta- and volatility-based low risk anomalies are driven by return skewness. The empirical patterns concisely match the predictions of our model which generates skewness of stock returns via default risk. With increasing downside risk, the standard capital asset pricing model increasingly overestimates required equity returns relative to firms' true (skew-adjusted) market risk. Empirically, the profitability of betting against beta/volatility increases with firms' downside risk. Our results suggest that the returns to betting against beta/volatility do not necessarily pose asset pricing puzzles but rather that such strategies collect premia that compensate for skew risk.
Inference on Risk Premia in the Presence of Omitted Factors
AbstractWe propose a three-pass method to estimate the risk premia of observable factors in a linear
asset pricing model, which is valid even when the observed factors are just a subset of the true
factors that drive asset prices. Standard methods to estimate risk premia are biased in the
presence of omitted priced factors correlated with the observed factors. We show that the risk
premium of a factor can be identied in a linear factor model regardless of the rotation of the other
control factors as long as they together span the space of true factors. Motivated by this rotation
invariance result, our approach uses principal components to recover the factor space and combines the estimated principal components with each observed factor to obtain a consistent estimate of its risk premium. This methodology also accounts for potential measurement error in the observed factors and detects when such factors are spurious or even useless. The methodology exploits the blessings of dimensionality, and we therefore apply it to a large panel of equity portfolios to estimate risk premia for several workhorse linear models.
- G1 - Asset Markets and Pricing