# Cedric Ehouarne

Cedric Ehouarne, Ph.D.

Director, Senior Quantitative Finance Manager

Bank of America | Model Risk Management

Manhattan, New York City, NY

+1 (412) 708-9668

I am a macroeconomist with expertise in credit risk and research interests in consumer finance and computational economics. I manage a team of quants in Model Risk Management at Bank of America. My team focuses on reviewing and challenging retail risk models used for CCAR, CECL, PPNR, and risk appetite. The material on this website does not reflect the views of the bank and mostly reflects my work at Carnegie Mellon University during my Ph.D. studies.

I graduated from Carnegie Mellon University in May 2016 with a Ph.D. in Economics and received the Alexander Henderson Award for Excellence in Economic Theory. My thesis studied the U.S. business cycle at the cross-sectional level to shed new light on classical puzzles in Macro-Finance. My dissertation committee members were Lars-Alexander Kuehn (chair), Sevin Yeltekin, Ariel Zetlin-Jones, and Daniele Coen-Pirani.

Interests

Macroeconomics: business cycle, credit risk, unemployment, housing market, asset bubbles

Consumer Finance: household leverage, optimal default, portfolio decisions, incomplete markets

Computational Economics: heterogeneous-agent models (households, firms), optimization problems

Research

“Cluster Search Optimization” (toy project)

This article proposes a stochastic-search, population-based algorithm for solving large-scale optimization problems where the objective function is both highly-non linear and computationally costly to evaluate. Common examples are encountered in economics and finance for solving decision problems with multiple controls or estimating structural models by a simulated method of moments. The key novelty is that the algorithm optimally trades off the exploration for multiple local optima with the convergence toward a precise solution by conducting a search on endogenous clusters. To do so, the algorithm operates in two phases. The first phase maximizes the number of disjoint search areas or clusters in order to cover as many local optima as possible given an initial sample of random draws. Each cluster is formed of multiple ball-shaped search neighborhoods. The second phase shrinks the size of each cluster by reducing the radius of their associated neighborhoods. The size reduction is chosen such that the search area is spatially bounded by candidate points previously found sub-optimal.

This figure illustrates the mechanism of the cluster search algorithm and compares it with some alternatives. Each algorithm draws 40 points per iteration to locate the global maximum of a two-dimensional inverse paraboloid with several local optima. This function reaches its maximum of 100 at the coordinates (16.6805, 0.3652). The current maximum found by each algorithm is marked with a yellow circle.

In this example, the cluster search algorithm identifies up to six disjoint areas to explore. After a few iterations, the number of clusters gradually decreases and their radius collapses. During this process, the density of points in the active search area rises which improves the convergence toward the exact solution.

In contrast, the genetic algorithm converges slowly to the global optimum because the combination of two local optima often leads to a sub-optimal candidate. On the other hand, the particle swarm is attracted to local optima at the initial stage, which leads to an inefficient number of function evaluations. Lastly, the Monte Carlo method robustly finds the global maximum but is inefficient at converging toward a precise value because no information is shared between the different candidates.

The cluster search algorithm can be interpreted as a metaheuristic that mimics the expansion phase and decay phase of bacterial growth in a petri dish. The clustering component or expansion phase ensures that disjoint search neighborhoods are evenly spread out around the local optima while the shrinking component or decay phase increases the density of points in the search area to obtain a precise solution. This algorithm is promising for optimization problems where the objective function is noisy and difficult to evaluate.

“The Macroeconomics of Consumer Finance” (Working Paper)

This paper studies the macroeconomic effects of consumer credit conditions in an incomplete-market, general equilibrium model where households hold unsecured debt, and firms use labor. I show that consumer finance disturbances can cause business cycle fluctuations through a rich interplay between credit and labor risks. As unemployment rises, households are more likely to default, translating into tighter credit conditions that reduce their consumption and cause further unemployment. Such a feedback loop is reinforced by precautionary-saving motives among unconstrained households. Surprisingly, this mechanism can explain a large fraction of the volatility and persistence of U.S. unemployment even though it abstracts from traditional frictions like search or price stickiness.

“Misallocation Cycles” (Working Paper)

joint work with Lars-Alexander Kuehn and David Schreindorfer

The goal of this paper is to quantify the cyclical variation in firm-specific risk and study its aggregate consequences via the allocative efficiency of capital resources across firms. To this end, we estimate a general equilibrium model with firm heterogeneity and a representative household with Epstein-Zin preferences. Firms face investment frictions and permanent shocks, which feature time-variation in common idiosyncratic skewness. Quantitatively, the model replicates well the cyclical dynamics of the cross-sectional output growth and investment rate distributions. Economically, the model generates business cycles through inefficiencies in the allocation of capital across firms, which amounts to an average output gap of 4.5% relative to a frictionless model. These cycles arise because (i) permanent Gaussian shocks give rise to a power-law distribution in firm size and (ii) rare negative Poisson shocks cause time-variation in common idiosyncratic skewness. Despite the absence of firm-level granularity, a power law in the firm size distribution implies that large inefficient firms dominate the economy, which hinders the household's ability to smooth consumption.

“The Geography of Risk” (research project proposed as part of my Ph.D. thesis)

In this paper, I document and analyze the existence of countercyclical risk at the geographic level. Using state-level data from the Bureau of Labor Statistics over the period 1976-2015, I show that the standard deviation of unemployment rates across U.S. states is countercyclical, highly volatile, and asymmetric over the Business Cycle: it jumps at the onset of a recession and slowly decays during the recovery. A look at the FRNY regional data from 1999 to 2014 reveals that this geographic labor risk is associated with spikes in the interstate dispersion of default rates, delinquency rates, and negative growth rates in total consumer debt. To understand the forces behind the joint dynamics of employment, credit, and default across time periods and U.S. states, I will develop and numerically solve a fully dynamic stochastic general equilibrium with incomplete markets, tradable and non-tradable goods, and two layers of heterogeneity – one at the household level, and another one at the state level. I model geographic volatility shocks as idiosyncratic labor productivity jumps with state-level time-varying frequencies and will investigate their ability to generate economy-wide cycles through interstate trade spillovers. I will then use the theory to quantify the magnitude of these spillovers during the recessionary episode of 2007–09.

Teaching

Instructor at Carnegie Mellon University

Principles of Economics, Undergraduate, Summer 2013. Teaching evaluation: 4.36/5.00

Teaching Assistant at Carnegie Mellon University / University of Quebec at Montreal

MBA / Graduate level:

- Global Economics (Spring 2013, Fall 2013, Spring 2014, Fall 2014, Fall 2015)

- Finance I (Summer 2012)

- Foundations of Financial Econometrics (Winter 2010)

Undergraduate level:

- Derivative Securities (Fall 2014, Fall 2015)

- Intermediate Microeconomics (Spring 2014)

- Investment Analysis (Fall 2013)

- Principles of Economics (Spring 2013)

- Mathematics for Economists I (Winter 2010)

- Intermediate Microeconomics (Fall 2009)

- Mathematics for Economists III (Fall 2009)

- Economic Analysis for Engineers (Winter 2009)