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...

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 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...

Inspired by the preferred-habitat theory, we propose parametric interest rate models that split the term structure into segments. The proposed models are compared to successful term structure benchmarks based on out-of-sample forecasting exercises using US Treasury data. We show that...

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...

In this paper we approximate the risk factors of a polynomial arbitrage-free dynamic term structure model by running a sequential set of linear regressions independent across time. This approximation avoids the cost of a full optimization procedure allowing for a simple method to extract the...