Christophe Pérignon

Professor of Finance
Associate Dean for Research



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 obtained a Ph.D. in Finance from the Swiss Finance Institute and was 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), as well as several articles in top management (Management Science) or general-science journals (Science). In 2014, he received the Europlace Award for the Best Young Researcher in Finance. 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 verifying the reproducibility of the results displayed in scientific articles (300+ verifications). Christophe is also the founder of RAMP-UP, a 'science-job' matching platform for students and professors.


The Fairness of Credit Scoring Models NEW
Management Science, forthcoming (with C. Hurlin and S. Saurin)

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

The Role of Third-Party Verification in Research Reproducibility NEW
Harvard Data Science Review, 2024 [journal link]

Non-Standard Errors
Journal of Finance, 2024 (with A. Menkveld, A. Dreber, F. Holzmeister, J. Huber, M. Johannesson, M. Kirchler, M. Razen, U. Weitzel et al.) My role: I designed and implemented the reproducibility verification policy of the #fincap project.

Reproducibility in Management Science
Management Science, 2024 (with M. Fišar, B. Greiner, C. Huber, E. Katok, A.I. Ozkes, and the Management Science Reproducibility Collaboration) My role: I am a member of the Management Science Reproducibility Collaboration.

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)

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

Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring (with S. Hué, C. Hurlin and S. Saurin)
Management Science, R&R; ESEM 2024 - Package in Python

In credit scoring, machine learning models are known to outperform standard parametric models. As they condition access to credit, banking supervisors and internal model validation teams need to monitor their predictive performance and to identify the features with the highest impact on performance. To facilitate this, we introduce the XPER methodology to decompose a performance metric (e.g., AUC, R^2) into specific contributions associated with the various features of a classification or regression model. XPER is theoretically grounded on Shapley values and is both model-agnostic and performance metric-agnostic. Furthermore, it can be implemented either at the model level or at the individual level. Using a novel dataset of car loans, we decompose the AUC of a machine-learning model trained to forecast the default probability of loan applicants. We show that a small number of features can explain a surprisingly large part of the model performance. Furthermore, we find that the features that contribute the most to the predictive performance of the model may not be the ones that contribute the most to individual forecasts (SHAP). We also show how XPER can be used to deal with heterogeneity issues and significantly boost out-of-sample performance.

Machine Learning and IRB Capital Requirements: Advantages, Risks, and Recommendations (with C. Hurlin)
December 2023

This survey proposes a theoretical and practical reflection on the use of machine learning methods in the context of the Internal Ratings Based (IRB) approach to banks' capital requirements. While machine learning is still rarely used in the regulatory domain (IRB, IFRS 9, stress tests), recent discussions initiated by the European Banking Authority suggest that this may change in the near future. While technically complex, this subject is crucial given growing concerns about the potential financial instability caused by the banks' use of opaque internal models. Conversely, for their proponents, machine learning models offer the prospect of better measurement of credit risk and enhancing financial inclusion. This survey yields several conclusions and recommendations regarding (i) the accuracy of risk parameter estimations, (ii) the level of regulatory capital, (iii) the trade-off between performance and interpretability, (iv) international banking competition, and (v) the governance and operational risks of machine learning models.

The Economics of Computational Reproducibility (with J.-E. Colliard and C. Hurlin)
December 2023

We investigate why economics displays a relatively low level of computational reproducibility. We first study the benefits and costs of reproducibility for readers, authors, and academic journals. Second, we show that the equilibrium level of reproducibility may be suboptimally low due to three market failures: a competitive bottleneck effect due to the competition between journals to attract authors, the public good dimension of reproducibility, and the positive externalities of reproducibility outside academia. Third, we discuss different policies to address these market failures and move out of a low reproducibility equilibrium. In particular, we show that coordination among journals could reduce by half the cost of verifying the reproducibility of accepted papers.


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

AI and bank capital requirements

Fairness equivalence in credit scoring: A framework to treat algorithms like prescription drugs

Biases in AI-enhanced human decisions



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

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Press Coverage

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