Curriculum vitae

Benoit Sylvain

Maître de conférences
LEDa

sylvain.benoitping@dauphinepong.fr
Tel : 0144054891
Bureau : P121

Biographie

Sylvain Benoit est maître de conférences en finance au sein du laboratoire d’économie de l’Université Paris-Dauphine. Il a obtenu un doctorant en sciences économiques à l’Université d’Orléans en 2014, reçu le trophée SAB pour la meilleure dissertation en finance durable en 2015 ainsi que le prix du meilleur article sur la régulation bancaire et financière lors de la conférence de l'Association Française de Finance (AFFi) en 2017. Sylvain a effectué plusieurs séjours de recherche à l’étranger notamment à l’University of California Santa Cruz (2011) et à la Cass Business School (2014). Sa recherche se concentre essentiellement sur le risque systémique, à la fois d’un point de vue théorique et empirique, et a été présentée à des conférences très sélectives comme l’European Finance Association (EFA), la Northern Finance Association (NFA) ou l’European Meeting of the Econometric Society. Deux de ses articles de recherche ont été publié dans la Review of Finance. Plus généralement, ses centres d’intérêts sont la régulation financière, la stabilité financière ainsi que l’économétrie financière. Par ailleurs, Sylvain a participé en tant qu’assistant de recherche au projet RunMyCode, désormais Unité Mixte de Services, et partage systématiquement ses codes ainsi que les données utilisées dans ses articles de recherche afin de promouvoir la transparence et la reproductibilité de la recherche scientifique.

Publications

Articles

Benoit S., Colliard J-E., Hurlin C., Pérignon C. (2017), Where the Risks Lie: A Survey on Systemic Risk, Review of Finance, 21, 1, p. 109-152

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.

Benoit S., Hurlin C., Pérignon C. (2015), Implied Risk Exposures, Review of Finance, 19, 6, p. 2183-2222

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.

Benoit S. (2014), Where is the System?, International Economics, 138, p. 1-27

The aim of this paper is to determine the optimal size of the system (global, supranational or national) when measuring the systemic importance of a bank. Since 2011, the Basel Committee on Banking Supervision (BCBS) has tagged global systemically important banks (G-SIBs) and has imposed a higher regulatory capital of loss absorbency (HLA) requirement. However, the identification of G-SIBs may overlook banks with major domestic systemic importance, i.e. the domestic systemically important banks (D-SIBs). This paper describes how to adjust market-based systemic risk measures to identify D-SIBs. In an empirical analysis within the eurozone, I show that (i) the SRISK methodology produces similar rankings whatever the system used. However, (ii) the SRISK values greatly vary across systems, which calls for imposing the higher of either D-SIB or G-SIB HLA requirements. Finally, (iii) the ?CoVaR methodology is extremely sensitive to the choice of the system.

Documents de travail

Benoit S., Hurlin C., Pérignon C. (2017), Pitfalls in Systemic-Risk Scoring, HEC Paris Research Paper, 73

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.

Benoit S., Dudek J., Sharifova M. (2013), Identifying SIFIs: Toward the Simpler Approach, Document de travail du LEDA, Université Paris Dauphine, 33

Systemic risk measures generally aim to identify systemically important financial institutions (SIFIs) that would allow regulators to allocate macro-prudential capital requirements in order to reduce risk stemming from such institutions. Among widely-cited are the measures of tail dependence in financial institutions' equity returns, such as ?CoVaR of Adrian and Brunnermeier (2011) and Marginal Expected Shortfall (MES) of Acharya et al. (2010). This paper compares nonlinear and linear approaches to modeling return dependence in the estimation of the ?CoVaR and MES. Our results show that while the refined and complicated estimation techniques are able to produce more accurate value of institution's systemic risk contribution they do not greatly improve in terms of identifying SIFIs compared to simpler linear estimation method. Modeling dependence linearly sufficient to identify and rank SIFIs.

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