PERIGNON Christophe

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In credit markets, screening algorithms discriminate between good-type and bad-type borrowers. This is their raison d’être. However, by doing so, they also often discriminate between individuals sharing a protected attribute (e.g. gender, age, race) and the rest of the population. In this paper, we show how to test (1) whether there exists a statistical significant difference in terms of rejection rates or interest rates, called lack of fairness, between protected and unprotected groups and (2) whether this difference is only due to credit worthiness. When condition (2) is not met, the screening algorithm does not comply with the fair-lending principle and can be qualified as illegal. 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.

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We derive several popular systemic risk measures in a common framework and show that they can be expressed as transformations of market risk measures (e.g. beta). We also derive conditions under which the different measures lead to similar rankings of systemically important financial institutions (SIFIs). In an empirical analysis of US financial institutions, we show that (1) different systemic risk measures identify different SIFIs and that (2) firm rankings based on systemic risk estimates mirror rankings obtained by sorting firms on market risk or liabilities. One-factor linear models explain most of the variability of the systemic risk estimates, which indicates that systemic risk measures fall short in capturing the multiple facets of systemic risk.

In this paper, we identify several shortcomings in the systemic-risk scoring methodology currently used to identify and regulate Systemically Important Financial Institutions (SIFIs). Using newly-disclosed regulatory data for 119 US and international banks, we show that the current scoring methodology severely distorts the allocation of regulatory capital among banks. We then propose and implement a methodology that corrects for these shortcomings and increases incentives for banks to reduce their risk contributions.

In this article, we propose a reflection on the contribution of Machine Learning techniques and New Data to credit risk modeling. Credit scoring was historically one of the first fields of application of Machine Learning techniques. Today, these techniques make it possible to exploit "new" data made available by the digitalization of customer relations and social networks. The combination of the emergence of new methodologies and new data has thus structurally modified the credit industry and favored the emergence of new players. First, we analyze the contribution of Machine Learning algorithms with a constant information set. We show that there are productivity gains linked to these new approaches but that the gains in credit risk prediction remain modest. Second, we evaluate the contribution of this "datadiversity", whether or not these new data are exploited by Machine Learning techniques. It turns out that some of these data reveal weak signals that significantly improve the quality of the assessment of borrowers' creditworthiness. At the micro level, these new approaches promote financial inclusion and access to credit for the most fragile borrowers. However, Machine Learning applied to these data can also lead to bias and discrimination.

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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.

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We study the effect of dividend taxes on the payout and investment policies of publicly listed firms. To do so, we exploit a unique setting in Switzerland where, following the corporate tax reform of 2011, some but not all firms were suddenly able to pay tax-exempt dividends to their shareholders. Using a difference-in-differences specification, we show that treated firms swiftly and permanently increase their dividend payout by around 30% compared to control firms after the tax cut. When studying the effect of agency conflicts, we show that the impact on the payout is less pronounced for firms in which the controlling shareholders have more voting rights than cash-flow rights. We find a significant positive abnormal stock return after the announcement of the payment of a tax-exempt dividend. However, reducing dividend taxes does not boost investment. This is due to a significant drop in retained earnings and to the fact that equity issuances do not surge after the tax cut. Our evidence is consistent with models where the marginal source of finance is retained earnings, and inconsistent with the neoclassical theory of dividend taxation.

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This thesis studies the behavior of investors in open-end mutual funds and its implications for liquidity risk. The objective of this research is to help fund managers avoid the "fund run" scenario where they suddenly lose their clients. The first step of this study is to collect a new database that records investors' "micro-transactions". This allows us to analyze their behavior at the individual level and to conduct three research papers on this topic. In the first paper, we develop a self-exciting counting model that captures stylized facts of the fund flow series. From this, we show a fund liability risk that is different from the asset risk already documented in the previous literature. We also identify a contagion of liquidity shocks across different clients in the same fund. In the next chapter, we study the investment horizons of individual clients. These horizons are strongly related to investor characteristics and economic conditions. We also show that fund managers face a premature exit risk related to the shortening of their clients' investment horizons. We then observe heterogeneity among investors: long-term investors behave differently than short-term investors. Finally, in the last chapter, we focus on rebalancing activities. We find that many investors hold a portfolio containing several funds and rebalance it to keep the same asset allocation.

We identify several shortcomings in the systemic-risk scoring methodology currently used to identify and regulate Systemically Important Financial Institutions (SIFIs). Using newly-disclosed regulatory data for 119 US and international banks, we show that the current scoring methodology severely distorts the allocation of regulatory capital among banks. We then propose and implement a methodology that corrects for these short-comings and increases incentives for banks to reduce their risk contributions. Unlike the current scores, our adjusted scores are mainly driven by risk indicators directly under the control of the regulated bank and not by factors that are exogenous to the bank, such as exchange rates or other banks' actions.

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We review the extensive literature on systemic risk and connect it to the current regulatory debate. While we take stock of the achievements of this rapidly growing field, we identify a gap between two main approaches. The first one studies different sources of systemic risk in isolation, uses confidential data, and inspires targeted but complex regulatory tools. The second approach uses market data to produce global measures which are not directly connected to any particular theory, but could support a more efficient regulation. Bridging this gap will require encompassing theoretical models and improved data disclosure.

We show how to reverse-engineer banks’ risk disclosures, such as value-at-risk, to obtain an implied measure of their exposures to equity, interest rate, foreign exchange, and commodity risks. Factor implied risk exposures are obtained by breaking down a change in risk disclosure into a market volatility component and a bank-specific risk exposure component. In a study of large US and international banks, we show that (i) changes in risk exposures are negatively correlated with market volatility and (ii) changes in risk exposures are positively correlated across banks, which is consistent with banks exhibiting commonality in trading.

We review the extensive literature on systemic risk and connect it to the current regulatory debate. While we take stock of the achievements of this rapidly growing field, we identify a gap between two main approaches. The first one studies different sources of systemic risk in isolation, uses confidential data, and inspires targeted but complex regulatory tools. The second approach uses market data to produce global measures which are not directly connected to any particular theory, but could support a more efficient regulation. Bridging this gap will require encompassing theoretical models and improved data disclosure.

We review the extensive literature on systemic risk and connect it to the current regulatory debate. While we take stock of the achievements of this rapidly growing field, we identify a gap between two main approaches. The first one studies different sources of systemic risk in isolation, uses confidential data, and inspires targeted but complex regulatory tools. The second approach uses market data to produce global measures which are not directly connected to any particular theory, but could support a more efficient regulation. Bridging this gap will require encompassing theoretical models and improved data disclosure.

We empirically explore the fragility of wholesale funding of banks, using transaction level data on short-term, unsecured certificates of deposits in the European market. We do not observe any market-wide freeze during the 2008-2014 period. Yet, many banks suddenly experience funding dry-ups. Dry-ups predict, but do not cause, future deterioration of bank performance. Furthermore, in periods of market stress, banks with high future performance tend to increase reliance on wholesale funding. Thus, we fail to find evidence consistent with classical adverse selection models of funding market freezes. Our evidence is in line with theories highlighting heterogeneity between informed and uninformed lenders.

This book presents the concepts and modern techniques of financial market analysis and their application to portfolio management and, more generally, to risk management. It familiarizes the reader with: the institutional and operational framework, efficiency, risk, diversification, the various financial assets and their valuation models, and portfolio and risk management instruments. This 6th edition, which has been completely updated, sheds new light on a number of topics: . liquidity and counterparty risks . changes in financial regulations . financial innovation (high-frequency trading) . credit derivatives and structured products.Written with a constant concern for clarity, emphasizing applications rather than lengthy mathematical demonstrations, and illustrated with numerous examples, this book is intended for both students and finance professionals.

The capital of a bank is a key element of their soundness and in this thesis we examine its dynamics. Chapter 2 shows that the introduction of a deposit guarantee leads banks to increase their leverage. However, bank responses are heterogeneous: the effect decreases with size, systemicity and initial capitalization so that the largest banks and the initially least capitalized banks do not respond to the adoption of a deposit guarantee. Chapter 3 proposes a new measure to quantify the aggregate capitalization of banking sectors by considering market discipline and the regulatory framework. This index allows us to study how capital shortfalls relative to a bank-specific implied capital target induce aggregate credit fluctuations. The capitalization index is consistent with the Bank Lending Survey and is significantly correlated with future aggregate credit fluctuations, particularly during episodes of undercapitalization. Chapter 4 is not about bank capital but studies the impact of scientific standards and conventions (statistical significance levels) on the behavior of researchers. We identify irregularities in the distribution of t-stats from empirical papers and interpret these as the result of distorted incentives. Our identification allows us to separate publication bias from what we call inflation bias: the fact that researchers are tempted to inflate marginally rejected tests by choosing a "significant" specification.

We show how to reverse-engineer banks' risk disclosures, such as Value-at-Risk, to obtain an implied measure of their exposures to equity, interest rate, foreign exchange, and commodity risks. Factor Implied Risk Exposures (FIRE) are obtained by breaking down a change in risk disclosure into a market volatility component and a bank-specific risk exposure component. In a study of large US and international banks, we show that (1) changes in risk exposures are negatively correlated with market volatility and (2) changes in risk exposures are positively correlated across banks, which is consistent with banks exhibiting commonality in trading.

Systemic risk played a key role in the spread of the last global financial crisis. many measures of systemic risk have been developed to assess the contribution of a financial institution to system-wide risk. However, many questions regarding the ability of these measures to identify systemically important financial institutions (SIFIs) have been raised since systemic risk has multiple facets and some of them are difficult to identify, such as similarities between financial institutions.The general objective of this thesis in finance is therefore (i) to propose an empirical solution to identify SIFIs at the national level, (ii) to compare theoretically and empirically different measures of systemic risk and (iii) to measure changes in banks' risk exposures.First, chapter 1 proposes an adjustment of three market-based measures of systemic risk designed in an international framework to identify SIFIs at the national level. Second, chapter 2 introduces a common model in which several measures of systemic risk are expressed and compared. It is theoretically established that these systemic risk measures can be expressed in terms of traditional risk measures. Empirical application confirms these results and shows that these measures are not able to capture the multidimensional nature of systemic risk. Finally, Chapter 3 presents the Factor Implied Risk Exposures (FIRE) methodology for decomposing a change in a bank's risk measure into two components, the first representing market volatility and the second representing the bank's risk exposure. This chapter empirically illustrates that changes in risk exposures are positively correlated across banks, which is consistent with the fact that banks have similarities in their market positions.

The back cover states: "This book presents the concepts and modern techniques of financial market analysis and their application to portfolio management and, more generally, to risk management. It allows the reader to become familiar with: the institutional and operational framework, efficiency, risk, diversification, the various financial assets and their valuation models, and portfolio management and risk management instruments. This 6th edition, which has been completely updated, sheds new light on a number of topics: liquidity and counterparty risks . changes in financial regulations . financial innovation (high-frequency trading) . credit derivatives and structured products. Written with a constant concern for clarity, emphasizing applications rather than long mathematical demonstrations, and illustrated with numerous examples, this book is intended for both students and finance professionals.

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As most Exchange-Traded Funds (ETFs) engage in securities lending or are based on total return swaps, they expose their investors to counterparty risk. In this paper, we estimate empirically such risk exposures for a sample of physical and swap-based funds. We find that counterparty risk exposure is higher for swap-based ETFs, but that investors are compensated for bearing this risk. Using a difference-in-differences specification, we uncover that ETF flows respond significantly to changes in counter-party risk. Finally, we show that switching to an optimal collateral portfolio leads to substantial reduction in counterparty risk exposure.

This dissertation is composed of three distinct chapters, which aim to empirically analyze financial innovation in different fields: household finance, public finance, and the financial sector. The first chapter, written in collaboration with Claire Célérier, analyzes the increasing complexity of financial products offered to retail investors and suggests that this complexity is used by banks to reduce competitive pressure. The second chapter, written with Christophe Pérignon, focuses on toxic loans issued by local governments, and how their use is part of a political incentive system. The third chapter examines how the adoption of an innovative type of bond, representing conditional capital, can contribute to solving the dilemma of bank leverage.

We show how to reverse-engineer banks' risk disclosures, such as Value-at-Risk, to obtain an implied measure of their exposures to equity, interest rate, foreign exchange, and commodity risks. Factor Implied Risk Exposures (FIRE) are obtained by breaking down a change in risk disclosure into a market volatility component and a bank-specific risk exposure component. In a study of large US and international banks, we show that (1) changes in risk exposures are negatively correlated with market volatility and (2) changes in risk exposures are positively correlated across banks, which is consistent with banks exhibiting commonality in trading.

We derive several popular systemic risk measures in a common framework and show that they can be expressed as transformations of market risk measures (e.g., beta). We also derive conditions under which the different measures lead to similar rankings of systemically important financial institutions (SIFIs). In an empirical analysis of US financial institutions, we show that (1) different systemic risk measures identify different SIFIs and that (2) firm rankings based on systemic risk estimates mirror rankings obtained by sorting firms on market risk or liabilities. One-factor linear models explain most of the variability of the systemic risk estimates, which indicates that systemic risk measures fall short in capturing the multiple facets of systemic risk.

Using daily data on margins and variation margins for all clearing members of the Chicago Mercantile Exchange, we analyze the clearing house exposure to the risk of default by clearing members. We find that the major source of default risk for a clearing member is proprietary trading rather than trading by customers. Additionally, we show that extreme losses suffered by important clearing firms tend to cluster, which raises systemic risk concerns. Finally, we discuss how private insurance could be used to cover the loss from defaults by clearing members.

This paper presents a new method to validate risk models: the Risk Map. This method jointly accounts for the number and the magnitude of extreme losses and graphically summarizes all information about the performance of a risk model. It relies on the concept of a super exception, which is defined as a situation in which the loss exceeds both the standard Value-at-Risk (VaR) and a VaR defined at an extremely low probability. We then formally test whether the sequences of exceptions and super exceptions are rejected by standard model validation tests. We show that the Risk Map can be used to validate market, credit, operational, or systemic risk estimates (VaR, stressed VaR, expected shortfall, and CoVaR) or to assess the performance of the margin system of a clearing house.

We identify a potential bias in the methodology disclosed in July 2013 by the Basel Committee on Banking Supervision (BCBS) for identifying systemically important financial banks. Contrary to the original objective, the relative importance of the five categories of risk importance (size, cross-jurisdictional activity, interconnectedness, substitutability/financial institution infrastructure, and complexity) may not be equal and the resulting systemic risk scores are mechanically dominated by the most volatile categories. In practice, this bias proved to be serious enough that the substitutability category had to be capped by the BCBS. We show that the bias can be removed by simply standardizing each input prior to computing the systemic risk scores.

In this paper, we identify several shortcomings in the systemic-risk scoring methodology currently used to identify and regulate Systemically Important Financial Institutions (SIFIs). Using newly-disclosed regulatory data for 119 US and international banks, we show that the current scoring methodology severely distorts the allocation of regulatory capital among banks. We then propose and implement a methodology that corrects for these shortcomings and increases incentives for banks to reduce their risk contributions.

The objective of this thesis is to study the daily and weekly options on the Dutch market index AEX recently introduced by NYSE Euronext. We consider them along three main lines: first, the impact of their introduction on the volumes of existing longer-dated options. Second, we analyze the different types of investors who trade these options by distinguishing between market members, their customers and market makers. Finally, given the level of information and sophistication of investors who trade short-dated options, we examine the impacts of their trading on the volatility of the underlying market, the volatility of the AEX market index. Our main results reveal a substitution effect of new options for existing monthly options. We find a negative impact of daily and weekly options on monthly option volumes and a negative impact of the introduction of daily options on weekly option volumes. As for investors, we find that daily and weekly options are mainly traded by the clients of market members who turn out to be uninformed and unsophisticated. Regarding the impact of the new options on the market volatility of the underlying assets, we conclude that the level of volatility of the AEX index has increased following the introduction of daily and weekly options due to the fact that these new options are mainly traded by clients, who are uninformed investors.