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...

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...

This paper introduces a new tail risk measure based on the risk-neutral excess expected shortfall of a cross-section of stock returns. We propose a novel way to risk neutralize the returns without relying on option price information. Empirically, we illustrate our methodology by estimating a...

This paper proposes to extract tail risk from a risk-neutral mean-adjusted expected shortfall of high-frequency stock returns. Risk adjustment is based on a nonparametric estimator of the state price density that does not use option prices and relies solely on a stock index returns. This makes...

I estimate tail risk for Brazil from January 2001 to July 2020 and investigate the origins of tail risk variation. The tail risk measure peaks at stock market crashes, financial crises, political shocks and disaster events such as the coronavirus pandemic. Moreover, I find that tail risk is...