The conditional covariance between the market return and its variance, which we refer to as the leverage effect, is positively related to the variance risk premium. It contains incremental information about the variance risk premium after controlling for other return moments and additional variables suggested by the literature as determinants of the variance risk premium. This empirical finding is supported by theory: the pricing of volatility risk is the economic channel behind the strong positive relation between the two variables. We use this relation to construct a time series of the variance risk premium dating back to 1926. (JEL G12, G13) Received February 7, 2020; editorial decision September 01, 2021.

This paper studies the relation between 36 firm-level characteristics and stock returns in 48 countries using instrumented principal components analysis. A non-U.S. country-neutral conditional factor model performs well in describing risk and returns and generates small and statistically insignificant anomaly intercepts when allowing for three or more latent factors. The non-U.S. model performs better in emerging than in developed markets, while showing substantial differences across countries. On average, only 10 characteristics significantly contribute to the models’ performance. Market beta, momentum, and firm size characteristics instrument for systemic exposure in U.S. and non-U.S. models, while investment and book-to-market do not. (JEL G11, G12, G14, G15) Received January 28, 2021; editorial decision July 30, 2021

We show theoretically and empirically that the durations of corporate securities are monotonically related to their capital structure priority, with equity often having a negative duration. The magnitude of this effect increases with firm leverage. We use these insights to challenge existing results on stock-bond comovements and factor pricing. For example, though overlooked, higher leverage and lower priority reduce the correlation between corporate security and government bond returns, and these variables explain time-series and cross-sectional variation in correlations; traditional market model regressions significantly understate corporate bond betas; and regressions on standard term and default factors dramatically overstate interest rate and default risk.

I find that approximately 30% of price fluctuations in the Fama-French size and value factors are nonfundamental price pressures driven by correlated fund flows, which generate price movements that revert over time. Is this really demand-based price pressure? I show that the price effects happen exclusively in periods when mutual funds place trades, a fact that is difficult to explain using traditional mechanisms such as unobserved investor preference changes. The estimated price elasticity is also consistent with other studies. Overall, my findings show that a sizable fraction of size and value factor movements do not represent economic risk.

This paper evaluates whether economic uncertainty is consistent with the Merton (1973) intertemporal CAPM (ICAPM) theory. The economic uncertainty index of Jurado, Ludvigson and Ng (2015) consistently predicts a significant increase in stock market volatility. However, its innovation carries a statistically insignificant price of covariance risk in the cross-section, thereby failing to satisfy the Maio and Santa-Clara (2012) sign restrictions associated with the ICAPM. I also find robust evidence by using the level of the economic uncertainty index

We illustrate the role of left tail dependence—left tail mean (LTM)—in equity risk premium (ERP) predictability. LTM measures the average of pairwise left tail dependency among major equity sectors incorporating shocks imperceptible at the aggregate level. LTM, as well as the variance risk premium, significantly predicts the ERP in and out of sample, which is not the case with commonly used predictors. We find this predictability is the result of procyclical shocks’ reversals in a stable business cycle. This paper contributes to the ongoing debate on ERP predictability

Using proprietary data from a major fund data provider, we analyze the screening activity of investment consultants (ICs). We find that ICs frequently shortlist funds using threshold screens clustered at $500MM for AUM, 0% for benchmark-adjusted return, and quartiles for return percentile rank screens. Funds just above the $500MM AUM threshold get 14%–18% more page views and 5–9 pp greater flows over the next year compared to similar funds just below the threshold. Our results are consistent with ICs using a two-stage consider-then-choose decision-making process and cognitive reference numbers in selecting screening thresholds. (JEL G41, G11, G14, G29) Received January 8, 2021; editorial decision September 3, 2021.

We empirically study how inventory constraints of underwriters affect corporate bond offerings. Using underwriter-insurer-level transaction data, we find that a more constrained underwriter is more likely to place a bond and increases the allocation in the primary market to an insurer with a stronger preexisting relationship. The same underwriter is also more likely to buy back part of an allocation from the same insurer within 6 to 12 months after an offering. Overall, by “parking” inventory to relationship investors in the primary market, underwriters mitigate the effect of their inventory constraints on firms’ bond financing costs.

This paper explains that anticipated market liquidity is an important concern for arbitrageurs considering entry into a market, a concern that can generate self-fulfilling asset prices. In the model, fixed investment costs turn a market illiquid and generate an arbitrage opportunity. The worst-case return on pledged collateral constrains arbitrageurs’ leverage. The interaction between this return and arbitrageurs’ capital makes entry decisions complementary and can create multiple equilibria. When arbitrageurs enter with capital, the market becomes more liquid; the worst-case return rises; and more arbitrageurs enter with capital. When arbitrageurs withhold capital, the market stays illiquid; the worst-case return falls; and other arbitrageurs stay out

What distribution best characterizes the time series and cross-section of individual stock returns? To answer this question, we estimate the degree of cross-sectional return skewness relative to a benchmark that nests many models considered in the literature. We find that cross-sectional skewness in monthly returns far exceeds what this benchmark model predicts. However, cross-sectional skewness in long-run returns in the data is substantially below what the model predicts. We show that fat-tailed idiosyncratic events appear to be necessary to explain skewness in the data. (JEL, G10, G11, G12, G13, G14).