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. As an open-science evangelist, he contributed to develop innovative tools to help fellow researchers to make their research easier to use and to reproduce: in 2012, he co-founded RunMyCode.org, an academic website allowing researchers to share code and data; in 2019, he launched cascad, the first Certification Agency for Scientific Code and Data.

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

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

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.)

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 sample 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. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.

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

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)

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

The interpretability of credit-scoring models

Performance Interpretability

Algorithmic fairness

Studying and promoting research reproducibility in science

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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