When Moving‐Average Models Meet High‐Frequency Data: Uniform Inference on Volatility

We conduct inference on volatility with noisy high‐frequency data. We assume the observed transaction price follows a continuous‐time Itô‐semimartingale, contaminated by a discrete‐time moving‐average noise process associated with the arrival of trades. We estimate volatility, defined as the quadratic variation of the semimartingale, by maximizing the likelihood of a misspecified moving‐average model, with its order selected based on an information criterion. Our inference is uniformly valid over a large class of noise processes whose magnitude and dependence structure vary with sample size. We show that the convergence rate of our estimator dominates n 1/4 as noise vanishes, and is determined by the selected order of noise dependence when noise is sufficiently small. Our implementation guarantees positive estimates in finite samples.

A Macroeconomic Model With Financially Constrained Producers and Intermediaries

How much capital should financial intermediaries hold? We propose a general equilibrium model with a financial sector that makes risky long‐term loans to firms, funded by deposits from savers. Government guarantees create a role for bank capital regulation. The model captures the sharp and persistent drop in macro‐economic aggregates and credit provision as well as the sharp change in credit spreads observed during financial crises. Policies requiring intermediaries to hold more capital reduce financial fragility, reduce the size of the financial and non‐financial sectors, and lower intermediary profits. They redistribute wealth from savers to the owners of banks and non‐financial firms. Pre‐crisis capital requirements are close to optimal. Counter‐cyclical capital requirements increase welfare.

Learning With Heterogeneous Misspecified Models: Characterization and Robustness

This paper develops a general framework to study how misinterpreting information impacts learning. Our main result is a simple criterion to characterize long‐run beliefs based on the underlying form of misspecification. We present this characterization in the context of social learning, then highlight how it applies to other learning environments, including individual learning. A key contribution is that our characterization applies to settings with model heterogeneity and provides conditions for entrenched disagreement. Our characterization can be used to determine whether a representative agent approach is valid in the face of heterogeneity, study how differing levels of bias or unawareness of others' biases impact learning, and explore whether the impact of a bias is sensitive to parametric specification or the source of information. This unified framework synthesizes insights gleaned from previously studied forms of misspecification and provides novel insights in specific applications, as we demonstrate in settings with partisan bias, overreaction, naive learning, and level‐k reasoning.

Limit Points of Endogenous Misspecified Learning

We study how an agent learns from endogenous data when their prior belief is misspecified. We show that only uniform Berk–Nash equilibria can be long‐run outcomes, and that all uniformly strict Berk–Nash equilibria have an arbitrarily high probability of being the long‐run outcome for some initial beliefs. When the agent believes the outcome distribution is exogenous, every uniformly strict Berk–Nash equilibrium has positive probability of being the long‐run outcome for any initial belief. We generalize these results to settings where the agent observes a signal before acting.

Reputation and Sovereign Default

This paper presents a continuous‐time model of sovereign debt. In it, a relatively impatient sovereign government's hidden type switches back and forth between a commitment type, which cannot default, and an opportunistic type, which can, and where we assume outside lenders have particular beliefs regarding how a commitment type should borrow for any given level of debt and bond price. In any Markov equilibrium, the opportunistic type mimics the commitment type when borrowing, revealing its type only by defaulting on its debt at random times. The equilibrium features a “graduation date”: a finite amount of time since the last default, after which time reputation reaches its highest level and is unaffected by not defaulting. Before such date, not defaulting always increases the country's reputation. For countries that have recently defaulted, bond prices and the total amount of debt are increasing functions of the amount of time since the country's last default. For countries that have not recently defaulted (i.e., those that have graduated), bond prices are constant.

Bootstrap Standard Error Estimates and Inference

Asymptotic justification of the bootstrap often takes the form of weak convergence of the bootstrap distribution to some limit distribution. Theoretical literature recognized that the weak convergence does not imply consistency of the bootstrap second moment or the bootstrap variance as an estimator of the asymptotic variance, but such concern is not always reflected in the applied practice. We bridge the gap between the theory and practice by showing that such common bootstrap based standard error in fact leads to a potentially conservative inference.