We adopt a family of nonparametric Cressie-Read estimators to price options based on relative pricing using the underlying asset returns. We use option models with stochastic volatility and jumps to investigate the ability of each member in this family to price options with different moneynesses...

We characterize a set of risk-neutral measures associated with a comprehensive class of risk averse investors. From this set, we show how to construct option price bounds and recover the implied gamma: a parameter uniquely identifying the marginal investor pricing a given option. Empirically, we...

We show that net demand in the S&P 500 option market is fundamental to explain empirical puzzles related to the pricing kernel. When public investors (non-market makers) are exposed to variance risk by net-selling out-of-the-money (OTM) options, the pricing kernel is U-shaped, expected option...

Traditional asset pricing tests boil down to evaluating the maximum Sharpe ratio obtained from the factors in a given model. This implicitly assumes the linear stochastic discount factor (SDF) that prices the factors as the asset pricing model. We generalize this approach by considering a...

We propose a new class of performance measures for Hedge Fund (HF) returns based on a family of empirically identifiable stochastic discount factors (SDFs). The SDF-based measures incorporate no-arbitrage pricing restrictions and naturally embed information about higher-order mixed moments...

We develop a new approach to evaluate asset pricing models (APMs) based on Minimum Discrepancy (MD) projections that generalize the Hansen-Jagannathan (HJ, 1997) distance to account for an arbitrary number of moments of asset returns. The Minimum Discrepancy projections correct APMs to become...

We develop a new approach to identify model misspecifications based on Minimum Discrepancy (MD) projections that correct asset pricing models with the use of nonlinear functions of basis assets returns. These nonlinear corrections make our method more effective than the Hansen and Jagannathan...