We show that banks significantly underreport the risk in their trading book when they have lower equity capital. Specifically, a decrease in a bank’s equity capital results in substantially more violations of its self-reported risk levels in the following quarter. Underreporting is especially frequent during the critical periods of high systemic risk and for banks with larger trading operations. We exploit a discontinuity in the expected benefit of underreporting present in Basel regulations to provide further support for a causal link between capital-saving incentives and underreporting. Overall, we show that banks’ self-reported risk measures become least informative precisely when they matter the most. Received April 30, 2015; editorial decision October, 27 2016 by Editor Itay Goldstein.

We present CoMargin, a new methodology to estimate collateral requirements in derivatives central counterparties (CCPs). CoMargin depends on both the tail risk of a given market participant and its interdependence with other participants. Our approach internalizes trading externalities and enhances the stability of CCPs, thus reducing systemic risk concerns. We assess our methodology using proprietary data from the Canadian Derivatives Clearing Corporation that include daily observations of the actual trading positions of all of its members from 2003 to 2011. We show that CoMargin outperforms existing margining systems by stabilizing the probability and minimizing the shortfall of simultaneous margin-exceeding losses.

We analyze the flow of money between mutual fund categories, finding strong evidence of seasonality in investor risk aversion. Aggregate investor flow data reveal an investor preference for safe mutual funds in autumn and risky funds in spring. During September alone, outflows from equity funds average $13 billion, controlling for previously documented flow determinants (e.g., capital-gains overhang). This movement of large amounts of money between fund categories is correlated with seasonality in investor risk aversion, consistent with investors preferring safer (riskier) investments in autumn (spring). We find consistent evidence in Canada and also in Australia, where seasons are offset by 6 months.

We explore how asymmetric information in financial markets affects outcomes in product markets. Difference-in-difference tests around brokerage house merger/closure events (which increase asymmetric information through reductions in analyst coverage) indicate worse industry-adjusted sales growth for shocked firms than for their peers. Our results are consistent with Bolton and Scharfstein's (1990) tradeoff between investor agency concerns and predation risk. Further support is found in stronger treatment effects among firms with ex ante greater agency concerns, financing constraints, asymmetric information, and those operating in ex ante more competitive (fluid) product market spaces. Our results are concentrated in industries where we can clearly identify either net firm entry or exit.

This paper links the CEO's concerns for the current stock price to reductions in real investment. We identify short-term concerns using the amount of stock and options scheduled to vest in a given quarter. Vesting equity is associated with a decline in the growth of research and development and capital expenditure, positive analyst forecast revisions, and positive earnings guidance, within the same quarter. More broadly, by introducing a measure of incentives that is determined by equity grants made several years prior, and thus unlikely driven by current investment opportunities, we provide evidence that CEO contracts affect real decisions.

We propose a novel model of stochastic choice: the single-crossing random utility model (SCRUM). This is a random utility model in which the collection of preferences satisfies the single-crossing property. We o↵er a characterization of SCRUMs based on two easy-to-check properties: the classic Monotonicity property and a novel condition, Centrality. The identified collection of preferences and associated probabilities is unique. We show that SCRUMs nest both single-peaked and single-dipped random utility models and establish a stochastic monotone comparative result for the case of SCRUMs.

Why do people vote? We design a field experiment to estimate a model of voting “because others will ask”. The expectation of being asked motivates turnout if individuals derive pride from telling others that they voted, or feel shame from admitting that they did not vote, provided that lying is costly. In a door-to-door survey about election turnout, we experimentally vary (1) the informational content and use of a flyer pre-announcing the survey, (2) the duration and payment for the survey, and (3) the incentives to lie about past voting. The experimental results indicate significant social image concerns. For the 2010 Congressional election, we estimate a value of voting “to tell others” of about $$$15, contributing 2 percentage points to turnout. Finally, we evaluate a get-out-the-vote intervention in which we tell potential voters that we will ask if they voted.

We show that municipalities’ financial constraints can have a significant impact on local employment and growth. We identify these effects by exploiting exogenous upgrades in U.S. municipal bond ratings caused by Moody’s recalibration of its ratings scale in 2010. We find that local governments increase expenditures because their debt capacity expands following a rating upgrade. These expenditures have an estimated local income multiplier of 1.9 and a cost per job of $20,000 per year. Our findings suggest that debt-financed increases in government spending can improve economic conditions during recessions.

The availability of high frequency financial data has generated a series of estimators based on intra-day data, improving the quality of large areas of financial econometrics. However, estimating the standard error of these estimators is often challenging. The root of the problem is that traditionally, standard errors rely on estimating a theoretically derived asymptotic variance, and often this asymptotic variance involves substantially more complex quantities than the original parameter to be estimated. Standard errors are important: they are used to assess the precision of estimators in the form of confidence intervals, to create “feasible statistics” for testing, to build forecasting models based on, say, daily estimates, and also to optimize the tuning parameters. The contribution of this paper is to provide an alternative and general solution to this problem, which we call Observed Asymptotic Variance. It is a general nonparametric method for assessing asymptotic variance (AVAR). It provides consistent estimators of AVAR for a broad class of integrated parameters Θ = ∫ θt dt, where the spot parameter process θ can be a general semimartingale, with continuous and jump components. The observed AVAR is implemented with the help of a two-scales method. Its construction works well in the presence of microstructure noise, and when the observation times are irregular or asynchronous in the multivariate case. The methodology is valid for a wide variety of estimators, including the standard ones for variance and covariance, and also for more complex estimators, such as, of leverage effects, high frequency betas, and semivariance.