Using prices of both S&P 500 options and recently introduced VIX options, we study asset pricing implications of volatility risk. While pointing out the joint pricing kernel is not identified nonparametrically, we propose model-free estimates of marginal pricing kernels of the market return and...

Using both S&P 500 option and recently introduced VIX option prices, we study pricing kernels and their dependence on multiple volatility factors. We first propose nonparametric estimates of marginal pricing kernels, conditional on the VIX and the slope of the variance swap term structure. Our...

We study the disagreement of foreign exchange (FX) dealers using proprietary survey data on dealers' price quotes of short- and long-tenor currency derivatives. Dispersion among dealers is the highest at short tenors, where heterogeneous information is of great relevance, and is much lower at...

Inspired by Aumann and Serrano (2008) and Foster and Hart (2009), we propose risk-neutral options' implied measures of riskiness and investigate their significance in predicting the cross section of expected returns per unit of risk. The empirical analyses indicate a negative and significant...

This paper investigates whether multivariate crash risk is priced in the cross- section of expected stock returns. Motivated by a theoretical asset pricing model, we capture the multivariate crash risk of a stock by a combined measure based on its expected shortfall and its multivariate lower...

We derive lower and upper bounds on the conditional market autocorrelation index at various investment horizons without using the precise form of the utility function. The bounds are derived in terms of option prices and can be computed at daily frequency for any given horizon. The bounds...

We derive generalized bounds on conditional expected excess returns. The bounds deliver consistent expected returns for individual and index-type assets, are conditionally tight, account for all risk-neutral moments of returns, and outperform runner-up models for out-of-sample predictions....