We investigate the real effects of tail risk on firm-level capital investment. Using an option-implied tail measure, we find that tail risk impedes investment, controlling for investment opportunities and existing uncertainty measures. This relation is stronger for firms with higher tail exposure and crash risk. The tail risk effect is more pronounced for firms with higher investment irreversiblility and demand uncertainty, consistent with the real options mechanism. Corporate resilience built by hedging activities attenuates the effect, while stressed debt financing conditions weaken the resilience and amplify the effect. Tail risk also delays investment spikes. Two instrumental variables enhance the causal interpretation. Overall, we highlight the distinctive role of tail risk in shaping real investment.

We develop a model to evaluate the costs and benefits of disclosing information about audit quality. Specifically, we examine whether audit quality disclosure affects auditors' effort and investors' investment efficiency. In our setting, an auditor exerts unobservable effort to influence audit quality and is motivated by liability in the event of audit failure. The usefulness of audited financial reports for investors depends on both the quality of the underlying financial reporting (e.g., as embodied by GAAP) and the quality of auditors' reports (i.e., the likelihood with which audit evidence uncovers managerial misreporting). We show that audit quality disclosure increases auditors' effort incentives if and only if the underlying financial reporting quality is relatively weak. We also show that such disclosure can actually reduce investment efficiency. Our analyses contribute to the debate about policies aimed at improving audit transparency.

We introduce a novel weighted least squares approach to estimate daily realized covariation and microstructure noise variance using high-frequency data. We provide an asymptotic theory and conduct a comprehensive Monte Carlo simulation to demonstrate the desirable statistical properties of the new estimator, compared with existing estimators in the literature. Using high-frequency data of 27 DJIA constituting stocks over a period from 2014 to 2020, we confirm that the new estimator performs well in comparison with existing estimators. We also show that the noise variance extracted based on our method can be used to improve volatility forecasting and asset allocation performance.