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 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 are derivatives markets, banking, risk management and financial regulation. His research has been published in top finance journals including the Journal of Finance, Journal of Financial Economics, Review of Financial Studies, Journal of Business, Journal of Financial and Quantitative Analysis, and Review of Finance. In 2014, he received the Europlace Award for the Best Young Researcher in Finance. He co-founded, an academic website allowing researchers to share the code and data associated with their scientific papers and cascad, the first Certification Agency for Scientific Code and Data.


Certify reproducibility with confidential data NEW
, 2019 (with K. Gadouche, C. Hurlin, R. Silberman, and E. Debonnel)

The Counterparty Risk Exposure of ETF Investors NEW
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


The Private Production of Safe assets (with M. Kacperczyk and G. Vuillemey) EFA 2018, AFA 2019, R&R Journal of Finance

Do claims on the private sector serve the role of safe assets? We answer this question using high-frequency panel data on prices and quantities of certificates of deposit (CDs) issued in Europe. We find that only very short-term private securities benefit from a premium for safety. Further, we show that the issuance of short-term CDs strongly responds to measures of safety demand. Our identification strategy uses a combination of (1) exclusion restrictions in a structural model of demand/supply equations, and (2) an instrumental variables approach. The private production of safe assets is stronger for issuers with high creditworthiness, and breaks down during episodes of market stress even though the market does not freeze. We conclude that even very short-term private assets are sensitive to changes in the information environment.

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

We study the effect of dividend taxes on the payout and investment policy of publicly listed firms. To do so, we exploit a unique setting in Switzerland where some, but not all, firms were suddenly able to pay tax-exempt dividends to their shareholders following the corporate tax reform of 2011. Using a difference-in-differences specification, we show that treated firms permanently increase their payout by around 30% compared to control firms after the tax cut. The rise in dividends is not compensated by an equally-sized reduction in share repurchases. In the cross-section, the impact on payout is much less pronounced in firms where the controlling shareholders have more voting rights than cash-flow rights. However, reducing dividend taxes does not boost investment. The tax-inelasticity of investment is due to a significant drop in retained earnings and to the fact that equity issuances do not surge after the tax cut. Overall, we interpret our findings as evidence for the distortive effect of dividend taxes on the allocation of capital across firms.

Reproducibility Certification in Economics Research (with C. Hurlin) NEW

Reproducibility is key for building trust in research, yet it is not widespread in economics. We show how external certification can improve reproducibility in economics research. Such certification can be conducted by a trusted third party or agency, which formally tests whether a given result is indeed generated by the code and data used by a researcher. This additional validation step significantly enriches the peer-review process, without adding an extra burden on journals or unduly lengthening the publication process. We show that external certification can accommodate research based on confidential data. Lastly, we present an actual example of external certification.

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

In this article, we discuss the contribution of machine learning techniques and new data sources (new data) to credit-risk modelling. Credit scoring was historically one of the first fields of application of machine learning techniques. Today, these techniques permit to exploit new sources of data made available by the digitalization of customer relationships and social networks. The combination of the emergence of new methodologies and new data has structurally changed the credit industry and favored the emergence of new players. First, we analyse the incremental contribution of machine learning techniques per se. We show that they lead to significant productivity gains but that the forecasting improvement remains modest. Second, we quantify the contribution of the "datadiversity", whether or not these new data are exploited through machine learning. It appears that some of these data contain weak signals that significantly improve the quality of the assessment of borrowers’ creditworthiness. At the microeconomic level, these new approaches promote financial inclusion and access to credit for the most vulnerable borrowers. However, machine learning applied to these data can also lead to severe biases and discrimination.

Work in Progress

The Economics of research reproducibility

Algorithmic fairness in finance and insurance

Machine Learning and credit scoring


Marchés Financiers:
Gestion de Portefeuille et des Risques
6e Edition, Dunod (2014)
Bertrand Jacquillat, Bruno Solnik & Christophe Pérignon

Order Here

Press Coverage

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