The QAU conducts research and analysis to support the Federal Reserve System's supervisory efforts, the System's development and implementation of regulatory policy, and the System's formation of monetary policy.

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Latest research

Credit Card Redlining
by Ethan Cohen-Cole

This paper evaluates the presence of racial disparities in the issuance of consumer credit. Using a unique and proprietary database of credit histories from a major credit bureau, this paper links location-based information on race with individual credit files. After controlling for the influence of such other place-specific factors as crime, housing vacancy rates, and general population demographics, the paper finds qualitatively large differences in the amount of credit offered to similarly qualified applicants living in Black versus White areas. An instrumental variables approach allows the paper to distinguish between issuer-provided credit (supply) and utilization of credit (demand), where instruments for demand are derived from social theory à la Veblen (i.e., `keeping up with the Joneses'). The results suggest that the observed differences in credit lines by racial composition of neighborhood are largely driven by issuer decisions rather than by demand.

Loss Distribution Estimation, External Data and Model Averaging
by Ethan Cohen-Cole and Todd Prono
Forthcoming, Journal of Financial Risk Management.

This paper will discuss a proposed method for the estimation of loss distribution using information from a combination of internally derived data and data from external sources. The relevant context for this analysis is the estimation of operational loss distributions used in the calculation of capital adequacy. We present a robust, easy-to-implement approach that draws on Bayesian inferential methods. The principal intuition behind the method is to let the data itself determine how they should be incorporated into the loss distribution. This approach avoids the pitfalls of managerial choice on data weighting and cut-off selection and allows for the estimation of a single loss distribution.

Demonstration Effects in Preventive Care
by Ritesh Banerjee, Ethan Cohen-Cole, and Giulio Zanella

Using a unique dataset composed of female employees at a large medical organization, this paper explores the role of social interactions among female co-workers and neighbors in the decision to obtain breast cancer screening exams. In our theoretical framework, the experience of other women is salient because it alters the tolerance for ambiguity about their own vulnerability, via a comparative ignorance effect. We find that the social multiplier ranges from 2 to 3: the equilibrium effect of an exogenous shock that impacts the probability of performing a mammogram is two to three times the shock itself. We perform a number of checks: among other things, these reveal (in agreement with the model and our intuition) that such a social effect is stronger for women whose job (according to the O*NET dictionary of occupations) offers more opportunities for social interaction, and weaker for individuals directly involved in health care, such as doctors and nurses.

Information Diffusion Based Explanations of Asset Pricing Anomalies
by Athanasios Bolmatis and Evan G. Sekeris

In this paper we develop information based factors which outperform other popular factors used in the multifactor pricing literature such as the Fama and French size and book-to-market factors. The first factor is based on the age of an asset, measured by the number of months since the asset’s IPO, while the second factor is based on the percentage of trading days an asset does not trade in a given year. Both factors attempt to capture the quality and speed of information diffusion on the market. Our information factors perform particularly well on momentum portfolios, which, Hong et al (2000) have shown to result from gradual-information diffusion. This gradual information diffusion explanation is consistent with the information argument underlying our factors, namely that, assets plagued with information problems can be miss-priced for sustained periods of time. Furthermore, our multifactor model successfully prices most industry portfolios and performs as well as the Fama and French model when pricing the 25 size/book-to-market sorted portfolios.

Asset Liquidity, Debt Valuation and Credit Risk
by Ethan Cohen-Cole

This paper presents a structural debt valuation model that links default probabilities and recovery rates of corporate securities to asset market liquidity. This linking is advantageous for risk management and regulation of financial institutions in that it provides a method of calibrating the relationship between probability of default (PD) and loss given default (LGD). Two innovations in the paper are the placing of the default point in a model of debt valuation into general equilibrium and conditioning this point on market factors such as asset liquidity. These allow one to derive implications on the correlation between various components of the model. Specifically, it finds two relationships between the probability of default (PD) and loss given default (LGD) of a debt instrument; temporal correlations are positive and cross-sectional ones negative. Such findings confirm the intuition of existing reduced form approaches and provide the ability to inspect other properties of the relationship that derive from theory. For example, one can use the model to forecast LGD. Some empirical validation of the theoretical results is provided.

Unpacking Social Interactions
by Ethan Cohen-Cole and Giulio Zanella
Forthcoming, Economic Inquiry.

As empirical work in identifying social e¤ects becomes more prevalent, researchers are beginning to struggle with identifying the composition of social interactions within any given reference group. In this paper, we present a simple econometric methodology for the separate identification of multiple social interactions. The setting under which we achieve separation is special, but is likely to be appropriate in many applications.

Model Uncertainty and the Deterrent Effect of Capital Punishment
by Ethan Cohen-Cole, Steven Durlauf, Jeffrey Fagan, Daniel Nagin
Forthcoming, American Economic and Law Review

The reintroduction of capital punishment after the end of the Supreme Court moratorium has permitted researchers to employ state level heterogeneity in the use of capital punishment to study deterrent effects. However, no scholarly consensus exists as to their magnitude. A key reason this has occurred is that the use of alternative models across studies produces differing estimates of the deterrent effect. Because differences across models are not well motivated by theory, the deterrence literature is plagued by model uncertainty. We argue that the analysis of deterrent effects should explicitly recognize the presence of model uncertainty in drawing inferences. We describe methods for addressing model uncertainty and apply them to understand the disparate findings between two major studies in the deterrence literature, finding that evidence of deterrent effects appears, while not nonexistent, is weak.

GARCH-Based Identification of Triangular Systems with an Application to the CAPM: Still Living with the Roll Critique
by Todd Prono

This paper presents a new method for identifying triangular systems of time-series data. Identification is the product of a bivariate GARCH process. Relative to the literature on GARCH-based identification, this method distinguishes itself both by allowing for a time-varying covariance and by not requiring a complete estimation of the GARCH parameters. Estimation follows OLS and standard univariate GARCH and ARMA techniques, or GMM. A Monte Carlo study of the GMM estimator is provided. The identification method is then applied in testing a conditional version of the CAPM.