I provide a new monthly cross-sectional measure of stock market tail risk, SCSTR, defined as the average of the daily cross-sectional tail risk, rather than the tail risk of the pooled daily returns within a month. Through simulations, I find that SCSTR better captures monthly tail risk rather than merely the tail risk on specific days within a month. In an extended period from 1964 until 2018, this difference is important in generating strong in- and out-of-sample predictability and performs better than the historical risk premium and other commonly-used predictors for short- and long-term horizons.

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.

Traditional methods of asset allocation (such as mean–variance optimization) are not adequate for option portfolios because the distribution of returns is non-normal and the short sample of option returns available makes it difficult to estimate their distribution. We propose a method to optimize a portfolio of European options, held to maturity, with a myopic objective function that overcomes these limitations. In an out-of-sample exercise incorporating realistic transaction costs, the portfolio strategy delivers a Sharpe ratio of 0.82 with positive skewness. This performance is mostly obtained by exploiting mispricing between options and not by loading on jump or volatility risk premia.

The paper uses bids submitted by primary dealer banks at auctions of sovereign bonds to quantify the price elasticity of demand. The price elasticity of demand correlates strongly with the volatility of returns of the same bonds traded in the secondary market but only weakly with their bid-ask spread. It predicts same-bond post-auction returns in the secondary market, even after controlling for pre-auction volatility. The evidence suggests that the price elasticity of demand is associated with the magnitude of price pressure in the secondary market around auction days and proxies for primary dealer risk-bearing capacity.