REVISION: Conditional Risk in Global Stock Returns
We estimate the premium associated with time-varying market betas without using rolling betas or instruments. Instead, we use a new conditional-risk factor, which is a market timing strategy defined as the unexpected return on the market times the ex ante price of risk. The factor is a powerful tool for documenting a global effect of conditional risk on stock returns: across 23 developed countries, all major equity risk factors load on our conditional-risk factor with the right sign, meaning their alpha can partly be explained by the time variation in their market betas. The conditional-risk factor explains 50% more alpha than traditional methods that use rolling betas to capture conditional risk.
REVISION: Duration-Driven Returns
We propose a duration-based explanation for the major equity risk factors, including value, profitability, investment, low risk, and payout factors. Both in the US and globally, these factors invest in firms that earn most of their cash flows in the near future. The factors could therefore all be driven a premium on near-future cash flows. We test this hypothesis using a novel dataset of single-stock dividend futures, which are claims on annual dividends of individual firms. Consistent with our hypothesis, risk-adjusted returns are higher on near- than on distant-future cash flows. In addition, firm-level characteristics do not predict returns on the cash flows once controlling for maturity.
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. The counter-cyclical variation is 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. More generally, any one-factor model will fail to explain both the average downward slope and the counter-cyclical variation. I therefore introduce a new model with two priced risk factors to solve the puzzle.
REVISION: Higher-Moment Risk
We estimate and analyze the ex ante higher order moments of stock market returns. We document that even and odd higher-order moments are strongly negatively correlated, creating periods where the return distribution is riskier because it is more left-skewed and fat tailed. Such higher-moment risk is negatively correlated with variance and past returns, meaning that it peaks during calm periods. The variation in higher-moment risk is large and causes the probability of a two-sigma loss on the market portfolio to vary from 3.3% to 11% percent over the sample, peaking in calm periods such as just before the onset of the financial crisis. In addition, we argue that an increase in higher- moment risk works as an "uncertainty shock" that deters firms from investing. Consistent with this argument, more higher-moment risk predicts lower future industrial production.
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.