Publications

This study analyzes the impact of international climate agreements-specifically the Kyoto Protocol-on the trade in environmental goods over the period 1997-2021. Using entropy balancing and a sample of 112 countries, we show that the Kyoto Protocol led to an overall increase in the trade of environmental goods among Annex B countries. During this period, the protocol notably stimulated imports, although it did not significantly boost exports prior to 2016. The analysis also highlights heterogeneities linked to countries' technological capacities, economic structures, and institutional characteristics.

This article has two primary objectives: 1) to propose a new parametric model of volatility and 2) to identify the days with the most significant volatility shocks, while exploring the events that triggered these shocks. We analyze financial volatility during the global financial crisis and the COVID-19 pandemic, utilizing high-frequency financial data and an improved model, which incorporates asymmetry and handles outliers, to estimate volatility and determine the timing of the largest shocks within the day. For instance, for the global financial crisis, major volatility shocks coincide with events such as the bankruptcy of Lehman Brothers and the failure of the Emergency Economic Stabilization Act. However, the largest shock occurred on February 27, 2007, which, despite coinciding with market events like a Chinese stock market crash and Freddie Mac's tighter subprime loan policy, was primarily caused by a computer glitch. Our analysis underscores the value of high-frequency data in modeling financial volatility and identifying the key events driving volatility shocks.

Le Parlement néerlandais a approuvé une réforme majeure des régimes de retraite professionnelle en juin 2023. Jusqu’alors, ces fonds de pension étaient gérés en capitalisation, mais garantissaient aux affiliés une prestation définie, analogue aux annuités des régimes de retraite fonctionnant en répartition. Bien que les règles de calcul des prestations aient été progressivement durcies pour les salariés, la garantie d’une pension certaine pesait sur les employeurs qui supportaient in fine les risques de placement financier et de longévité des affiliés. La réforme contraint les régimes de retraite professionnelle à transformer les fonds à prestations définies en fonds collectifs à cotisations définies (collective defined contributions – CDC) d’ici le 1er janvier 2028. Ses principaux objectifs visent à réduire les risques financiers pour les employeurs, à mieux les répartir entre les générations et à assurer la soutenabilité des régimes dans un contexte de vieillissement de la population et d’incertitude économique. Notre article se propose d’analyser cette réforme sous l’angle de l’équité intergénérationnelle, dont l’objectif est de répartir équitablement les risques de marché et de longévité entre actifs et retraités, pour éviter que les fluctuations économiques ou l’allongement de l’espérance de vie ne pèsent uniquement sur une seule génération, en particulier sur les jeunes.

This chapter examines how age influences the contact effect in shaping tolerance toward immigrants, using French data from the 2017 European Values Survey. While contact theory posits that frequent interactions with immigrants generally increase tolerance, the findings reveal a nuanced relationship. A higher share of immigrants in a district correlates with greater tolerance, but this effect diminishes for young immigrants (15–24 years), often leading to lower tolerance. Interestingly, respondents’ age does not significantly affect their reaction to immigrant proportions. These results challenge utilitarian perspectives, suggesting that hostility toward young immigrants is rooted in personal experiences rather than economic logic.

The evolution of credit markets, driven by regulatory reforms, trading electronification, and the increasing availability of data, has reshaped the landscape for asset managers. This thesis contributes to the advancement of systematic credit strategies by investigating credit risk dynamics, machine learning model design, and interest rate hedging techniques.The first contribution examines the impact of corporate action announcements—such as stock buybacks, dividends, and mergers & acquisitions—on credit default swap spreads. Quantifying abnormal spread movements around these events allows for the disentangling of wealth transfer from signaling effects, providing insights into the mechanisms driving credit market reactions.The second contribution focuses on improving asset ranking models for long-short portfolio construction. Traditional learning-to-rank algorithms favor top-ranked assets while neglecting underperformers, creating challenges for market-neutral strategies. This thesis proposes refined loss functions that enhance ranking symmetry across the full asset distribution, thereby improving portfolio balance and performance.The third contribution introduces a multi-task neural network framework that unifies asset ranking and portfolio weighting within a single optimization model. In contrast to conventional two-step approaches, this framework simultaneously optimizes both ranking and allocation, resulting in more efficient investment decisions.Finally, this thesis explores the relationship between equity and corporate bond markets by decomposing their correlation into credit risk and interest rate sensitivity components. Since these drivers vary across credit qualities and market conditions, this thesis proposes two adaptive interest rate hedging strategies. These strategies outperform traditional duration-convexity hedging by dynamically adjusting hedge ratios in response to evolving market regimes.These contributions collectively advance systematic credit investing by integrating financial expertise with advanced quantitative techniques. The findings offer asset managers practical methodologies for improving credit trading strategies in an increasingly data-driven and complex financial environment.