We derive tight pricing kernel restrictions from options with same‐day expiration (“0DTEs”). These restrictions concern the volatility of small and frequent asset price moves that the equity and options markets must agree on in a frictionless economy. Their violation leads to pseudo‐arbitrage opportunities, characterized by nontrivial reward‐to‐risk ratios over arbitrarily short horizons and achieved by a combined position in 0DTEs and the underlying asset. Empirically, we find no evidence of feasible pseudo‐arbitrage opportunities, as transaction costs, estimation risk, and short‐term volatility risk prevent investors from taking advantage of small and infrequent disagreements about volatility between equity and options markets.

We study short‐maturity (“weekly”) S&P 500 index options, which provide a direct way to analyze volatility and jump risks. Unlike longer‐dated options, they are largely insensitive to the risk of intertemporal shifts in the economic environment. Adopting a novel seminonparametric approach, we uncover variation in the negative jump tail risk, which is not spanned by market volatility and helps predict future equity returns. As such, our approach allows for easy identification of periods of heightened concerns about negative tail events that are not always “signaled” by the level of market volatility and elude standard asset pricing models.

We develop econometric tools for studying jump dependence of two processes from high-frequency observations on a fixed time interval. In this context, only segments of data around a few outlying observations are informative for the inference. We derive an asymptotically valid test for stability of a linear jump relation over regions of the jump size domain. The test has power against general forms of nonlinearity in the jump dependence as well as temporal instabilities. We further propose an efficient estimator for the linear jump regression model that is formed by optimally weighting the detected jumps with weights based on the diffusive volatility around the jump times. We derive the asymptotic limit of the estimator, a semiparametric lower efficiency bound for the linear jump regression, and show that our estimator attains the latter. The analysis covers both deterministic and random jump arrivals. In an empirical application, we use the developed inference techniques to test the temporal stability of market jump betas.