REVISION: Betting Against Correlation: Testing Theories of the Low-Risk Effect
We test whether the low-risk effect is driven by (a) leverage constraints and thus risk should be measured using beta vs. (b) behavioral effects and thus risk should be measured by idiosyncratic risk. Beta depends on volatility and correlation, where only volatility is related to idiosyncratic risk. We introduce a new betting against correlation (BAC) factor that is particularly suited to differentiate between leverage constraints vs. lottery explanations. BAC produces strong performance in the US and internationally, supporting leverage constraint theories. Similarly, we construct the new factor SMAX to isolate lottery demand, which also produces positive returns. Consistent with both leverage and lottery theories contributing to the low-risk effect, we find that BAC is related to margin debt while idiosyncratic risk factors are related to sentiment.
REVISION: Conditional Risk
We present a new direct methodology to study conditional risk, that is, the extra return compensation for time-variation in risk. We show theoretically that the conditional part of the CAPM can be captured by augmenting the standard market model with a conditional-risk factor, which is a specific market timing strategy. Both in the U.S. and global sample covering 23 countries, all major equity risk factors load on our conditional-risk factor, implying that each factor has a higher conditional market beta when the market risk premium is high or the market variance is low. Accordingly, these factor returns can be partly explained by conditional risk. Studying the economic drivers of these results, we find evidence that conditional risk arises from variation in discount rate betas (not cash flow betas) due to the endogenous effects of arbitrage trading.
REVISION: Time Variation of the Equity Term Structure
I document that the term structure of holding-period equity returns is counter-cyclical: it is downward sloping in good times, but upward sloping in bad times. This new stylized fact implies that long-maturity risk plays a central role in asset price fluctuations, consistent with theories of long-run risk and habit, but these theories cannot explain the average downward slope. At the same time, the cyclical variation is inconsistent with recent models constructed to match the average downward slope. I present the theoretical source of the puzzle and suggest a new model as a resolution. My model also shows that the counter-cyclical term structure has implications for real activity, which I verify empirically: in bad times, long-duration firms decrease their investment and capital-to-labor ratio relative to short-duration firms.
New: Higher-Moment Risk
We show how the market's higher order moments can be estimated ex ante using methods based on Martin (2017). These ex ante higher order moments predict future realized higher order moments, whereas trailing realized moments have little predictive power. Higher-moment risks move together in the sense that skewness becomes more negative when kurtosis becomes more positive. In addition, higher-moment risk is high when volatility is low, suggesting that risk doesn't go away - it hides in the tails. Higher-moment risk has significant implications for investors; for example, the tail loss probability of a volatility-targeting investor varies from 3.6% to 9.7%, entirely driven by changes in higher-moment risk. We empirically analyze the economic drivers of these risks, such as financial intermediary leverage, market and funding illiquidity, and potential bubbles.