Does the presence of arbitrageurs decrease equilibrium asset price volatility? I study an economy with arbitrageurs, informed investors, and noise traders. Arbitrageurs face a trade-off between “inference” and “arbitrage”: they would like to buy assets in response to temporary price declines—the arbitrage effect—but sell when prices decline permanently—the inference effect. In equilibrium, the presence of arbitrageurs increases volatility when the inference effect dominates the arbitrage effect. From a technical point of view, the paper offers closed form solutions to a dynamic equilibrium model with asymmetric information and non-Gaussian priors.

The availability of credit varies over the business cycle through shifts in the leverage of financial intermediaries. Empirically, we find that intermediary leverage is negatively aligned with the banks' Value-at-Risk (VaR). Motivated by the evidence, we explore a contracting model that captures the observed features. Under general conditions on the outcome distribution given by extreme value theory (EVT), intermediaries maintain a constant probability of default to shifts in the outcome distribution, implying substantial deleveraging during downturns. For some parameter values, we can solve the model explicitly, thereby endogenizing the VaR threshold probability from the contracting problem.

[We present a dynamic contracting model in which the principal and agent disagree about the resolution of uncertainty, and we illustrate the contract design in an application with Bayesian learning. The disagreement creates gains from trade that the principal realizes by transferring payment to states that the agent considers relatively more likely, changing incentives. The interaction between incentive provision and learning creates an intertemporal source of "disagreement risk" that alters optimal risk sharing. There is an endogenous regime shift between economies with small and large belief differences, and an early shock to beliefs can lead to large persistent differences in variable pay even after beliefs have converged. Under risk-neutrality, "selling the firm" to the agent does not implement the first-best because it precludes state-contingent trades.]

We present a dynamic contracting model in which the principal and the agent disagree about the resolution of uncertainty, and we illustrate the contract design in an application with Bayesian learning. The disagreement creates gains from trade that the principal realizes by transferring payment to states that the agent considers relatively more likely, a shift that changes incentives. In our dynamic setting, the interaction between incentive provision and learning creates an intertemporal source of "disagreement risk" that alters optimal risk sharing. An endogenous regime shift between economies with small and large belief differences is present, and an early shock to beliefs can lead to large persistent differences in variable pay even after beliefs have converged. Under risk-neutrality, "selling the firm" to the agent does not implement the first-best outcome because it precludes state-contingent trades.

Bank liquidity shortages associated with the growth of wholesale-funded credit intermediation has motivated the implementation of liquidity regulations. We analyze a dynamic stochastic general equilibrium model in which liquidity and capital regulations interact with the supply of risk-free assets. In the model, the endogenously time varying tightness of liquidity and capital constraints generates intermediaries’ leverage cycle, influencing the pricing of risk and the level of risk in the economy. Our analysis focuses on liquidity policies’ implications for households’ welfare. Within the context of our model, liquidity requirements are preferable to capital requirements, as tightening liquidity requirements lowers the likelihood of systemic distress without impairing consumption growth. In addition, we find that intermediate ranges of risk-free asset supply achieve higher welfare.

In a financial system in which balance sheets are continuously marked to market, asset price changes appear immediately as changes in net worth, and eliciting responses from financial intermediaries who adjust the size of their balance sheets. We document evidence that marked-to-market leverage is strongly procyclical. Such behavior has aggregate consequences. Changes in dealer repos – the primary margin of adjustment for the aggregate balance sheets of intermediaries – forecast changes in financial market risk as measured by the innovations in the Chicago Board Options Exchange Volatility Index VIX index. Aggregate liquidity can be seen as the rate of change of the aggregate balance sheet of the financial intermediaries.

We propose a measure of systemic risk, Δ CoVaR, defined as the change in the value at risk of the financial system conditional on an institution being under distress relative to its median state. Our estimates show that characteristics such as leverage, size, maturity mismatch, and asset price booms significantly predict Δ CoVaR. We also provide out-of-sample forecasts of a countercyclical, forward-looking measure of systemic risk, and show that the 2006:IV value of this measure would have predicted more than one-third of realized Δ CoVaR during the 2007–2009 financial crisis.

We show how to price the time series and cross section of the term structure of interest rates using a three-step linear regression approach. Our method allows computationally fast estimation of term structure models with a large number of pricing factors. We present specification tests favoring a model using five principal components of yields as factors. We demonstrate that this model outperforms the Cochrane and Piazzesi (2008) four-factor specification in out-of-sample exercises but generates similar in-sample term premium dynamics. Our regression approach can also incorporate unspanned factors and allows estimation of term structure models without observing a zero-coupon yield curve.

Financial intermediaries trade frequently in many markets using sophisticated models. Their marginal value of wealth should therefore provide a more informative stochastic discount factor (SDF) than that of a representative consumer. Guided by theory, we use shocks to the leverage of securities broker‐dealers to construct an intermediary SDF. Intuitively, deteriorating funding conditions are associated with deleveraging and high marginal value of wealth. Our single ‐factor model prices size, book‐to‐market, momentum, and bond portfolios with an R 2 of 77% and an average annual pricing error of 1%—performing as well as standard multifactor benchmarks designed to price these assets.

The availability of credit varies over the business cycle through shifts in the leverage of financial intermediaries. Empirically, we find that intermediary leverage is negatively aligned with the banks' Value-at-Risk (VaR). Motivated by the evidence, we explore a contracting model that captures the observed features. Under general conditions on the outcome distribution given by extreme value theory (EVT), intermediaries maintain a constant probability of default to shifts in the outcome distribution, implying substantial deleveraging during downturns. For some parameter values, we can solve the model explicitly, thereby endogenizing the VaR threshold probability from the contracting problem.