Christophe Pérignon

Professor of Finance
Associate Dean for Research

Email: perignon@hec.fr
www.hec.fr/perignon

Biography

Christophe Pérignon is Professor of Finance and Associate Dean for Research at HEC Paris, France. He is also the co-holder of the ACPR (Banque de France) Chair in Regulation and Systemic Risk. He holds a Ph.D. in Finance from the Swiss Finance Institute and has been a Post-Doctoral Fellow at the University of California at Los Angeles (UCLA). Prior to joining HEC Paris, he was an Assistant Professor of Finance at Simon Fraser University in Vancouver, Canada. His areas of research and teaching interests are in financial risk management and AI/machine learning. Christophe published a dozen of articles in top finance journals (Journal of Finance, Journal of Financial Economics, Review of Financial Studies, Journal of Business, Journal of Financial and Quantitative Analysis, and Review of Finance) or in general-science journals (Science). In 2014, he received the Europlace Award for the Best Young Researcher in Finance. Along with Christophe Hurlin, he co-founded RunMyCode, an online repository allowing researchers to share code and data associated with published papers (800,000+ individual visits); and cascad, a certification agency allowing researchers and academic journals to signal that their research has been reproduced by a third party (250+ studies verified). In 2022, Christophe launched Decilia Science, a data-science company producing and auditing machine-learning algorithms for companies, and RAMP-UP, the job matching platform for HEC Paris students and professors/staff.

 

Publications

What if Dividends Were Tax-Exempt? Evidence from a Natural Experiment
Review of Financial Studies, 2021 (with D. Isakov and J.P. Weisskopf)

The Private Production of Safe assets 
Journal of Finance
, 2021 (with M. Kacperczyk and G. Vuillemey)

Certify Reproducibility with Confidential Data
Science
, 2019 (with K. Gadouche, C. Hurlin, R. Silberman, and E. Debonnel)

Machine learning et nouvelles sources de données pour le scoring de crédit
Revue d'Economie Financière
, 2019 (with C. Hurlin)

The Counterparty Risk Exposure of ETF Investors
Journal of Banking and Finance
, 2019 (with C. Hurlin, G. Iseli, and S. Yeung)

Pitfalls in Systemic-Risk Scoring
Journal of Financial Intermediation
, 2019 (with S. Benoit and C. Hurlin)

Wholesale Funding Dry-Ups
Journal of Finance, 2018 (with D. Thesmar and G. Vuillemey)

The Political Economy of Financial Innovation: Evidence from Local Governments
Review of Financial Studies
, 2017 (with B. Vallée)

CoMargin
Journal of Financial and Quantitative Analysis,
2017 (with J. Cruz Lopez, J. Harris, and C. Hurlin)

Where the Risks Lie: A Survey on Systemic Risk
Review of Finance, 2017 (with S. Benoit, J.E. Colliard, and C. Hurlin)

Implied Risk Exposures
Review of Finance
, 2015 (with S. Benoit and C. Hurlin)

The Risk Map: A New Tool for Validating Risk Models
Journal of Banking and Finance
, 2013 (with G. Colletaz and C. Hurlin)

Derivatives Clearing, Default Risk, and Insurance
Journal of Risk and Insurance
, 2013 (with R. Jones)

The Pernicious Effects of Contaminated Data in Risk Management
Journal of Banking and Finance
, 2011 (with L. Frésard and A. Wilhelmsson)

The Level and Quality of Value-at-Risk Disclosure by Commercial Banks
Journal of Banking and Finance
, 2010 (with D. Smith)

Diversification and Value-at-Risk
Journal of Banking and Finance
, 2010 (with D. Smith)

Commonality in Liquidity: A Global Perspective
Journal of Financial and Quantitative Analysis
, 2009 (with P. Brockman and D. Chung)

How Common are Common Return Factors across Nyse and Nasdaq
Journal of Financial Economics, 2008 (with A. Goyal and C. Villa)

A New Approach to Comparing VaR Estimation Methods
Journal of Derivatives
, 2008 (with D. Smith)

Do Banks Overstate their Value-at-Risk?
Journal of Banking and Finance
, 2008 (with Z. Deng and Z. Wang)

Repurchasing Shares on a Second Trading Line
Review of Finance
, 2007 (with D. Chung and D. Isakov)

Testing the Monotonicity Property of Option Prices
Journal of Derivatives
, 2006

Sources of Time Variation in the Covariance Matrix of Interest Rates
Journal of Business
, 2006 (with C. Villa)

Working Papers

Explainable Performance (with S. Hué, C. Hurlin and S. Saurin) New!

We introduce the XPER (eXplainable PERformance) methodology to measure the specific contribution of the input features to the predictive or economic performance of a model. Our methodology offers several advantages. First, it is both model-agnostic and performance metric-agnostic. Second, XPER is theoretically founded as it is based on Shapley values. Third, the interpretation of the benchmark, which is inherent in any Shapley value decomposition, is meaningful in our context. Fourth, XPER is not plagued by model specification error, as it does not require re-estimating the model. Fifth, it can be implemented either at the model level or at the individual level. In an application based on auto loans, we find that performance can be explained by a surprisingly small number of features, XPER decompositions are rather stable across metrics, yet some feature contributions switch sign across metrics. Our analysis also shows that explaining model forecasts and model performance are two distinct tasks.

Computational Reproducibility in Finance: Evidence from 1,000 Tests (with O. Akmansoy, C. Hurlin, A. Menkveld, A. Dreber, F. Holzmeister, J. Huber, M. Johannesson, M. Kirchler, M. Razen, U. Weitzel)

We analyze the computational reproducibility of more than 1,000 empirical answers to six research questions in finance provided by 168 international research teams. Surprisingly, neither researcher seniority, nor the quality of the research paper seem related to the level of reproducibility. Moreover, researchers exhibit strong overconfidence when assessing the reproducibility of their own research and underestimate the difficulty faced by their peers when attempting to reproduce their results. We further find that reproducibility is higher for researchers with better coding skills and for those exerting more effort. It is lower for more technical research questions and more complex code.

Non-Standard Errors (with A. Menkveld, A. Dreber, F. Holzmeister, J. Huber, M. Johannesson, M. Kirchler, M. Razen, U. Weitzel et al.) Updated

My contribution: I co-designed and co-implemented the reproducibility verification policy of the #fincap project

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

The Fairness of Credit Scoring Models (with C. Hurlin and S. Saurin)

In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they also often discriminate between individuals sharing a protected attribute (e.g. gender, age, racial origin) and the rest of the population. In this paper, we show how (1) to test whether there exists a statistically significant difference between protected and unprotected groups, which we call lack of fairness and (2) to identify the variables that cause the lack of fairness. We then use these variables to optimize the fairness-performance trade-off. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, and improved for the benefit of protected groups.

The Economics of Research Reproducibility (with J.-E. Colliard and C. Hurlin) To be updated soon

We investigate why economics displays a relatively low level of research reproducibility. We first study the benefits and costs of reproducibility for readers (demand side) and authors (supply side), as well as the role of academic journals in matching both sides. Second, we prove that competition between journals to attract authors can lead to a suboptimally low level of reproducibility. Third, we show how to optimize the costs of reproducibility and estimate that reaching the highest level of reproducibility could cost USD 365 per paper. Finally, we discuss how leading journals can move economics out of a low-reproducibility equilibrium.

Reports

Reproducibility of scientific results in the EU. Publication Office of the EU. December 2020.

AuthorsBaker, Lee;  Lusoli, Wainer;  Jaśko, Katarzyna;  Parry, Vivienne;  Pérignon, Christophe;  Errington, Timothy;  Cristea, Ioana Alina;  Winchester, Catherine;  MacCallum, Catriona;  Šimko, Tibor

Work in Progress

Gender effects in AI-enhanced human decision-making

Interpreting model and economic performance

Algorithmic fairness and equivalence

Book

Marchés Financiers:
Gestion de Portefeuille et des Risques
6e Edition, Dunod
Bertrand Jacquillat, Bruno Solnik & Christophe Pérignon

Order Here

Press Coverage

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