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Financements intermédiés et informalité en Afrique

Gregory Mvogo, Mohamed Coulibaly, Modeste Abate, Novice Bakehe

Cet article analyse l'impact des financements intermédiés des institutions financières internationales sur le niveau d'informalité des économies africaines. En s'appuyant sur un panel de 39 pays africains couvrant la période 2000-2018, l'étude évalue dans quelle mesure ces financements, canalisés via des intermédiaires financiers locaux, constituent un levier de formalisation des activités économiques, en particulier des petites et moyennes entreprises. L'analyse mobilise une approche quasi-expérimentale combinant l'équilibrage entropique comme méthode principale et l'appariement par score de propension pour les tests de robustesse. Les résultats empiriques montrent que les financements intermédiés exercent un effet négatif et statistiquement significatif sur l'informalité, avec une réduction comprise entre 8 % et 13 % selon les spécifications. Ces résultats suggèrent que l'accès à des ressources longues et formalisées renforce l'inclusion financière, améliore la transparence et incite les entreprises à se conformer aux exigences institutionnelles. L'étude comble ainsi un vide dans la littérature en mettant en évidence le rôle spécifique des financements intermédiés comme instrument de soutien à la formalisation et au développement économique en Afrique.

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Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring

Sullivan Hué, Christophe Hurlin, Christophe Pérignon, Sébastien Saurin

Because they play an increasingly important role in determining access to credit, credit scoring models are under growing scrutiny from banking supervisors and internal model validators. These authorities need to monitor the model performance and identify its key drivers. To facilitate this, we introduce the explainable performance (XPER) methodology to decompose a performance metric (e.g., area under the curve (AUC), [Formula: see text]) into specific contributions associated with the various features of a forecasting 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 data set 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. Notably, 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 (Shapley additive explanation). Finally, we show how XPER can be used to deal with heterogeneity issues and improve performance. This paper was accepted by Kay Giesecke, finance. Funding: The authors thank the Institut Universitaire de France, the Autorité de contrôle prudentiel et de résolution Chair in Regulation and Systemic Risk, the HEC-Deloitte Chair on Artificial Intelligence for Business Innovation, the Excellence Initiative of Aix-Marseille University [A*MIDEX], and the French National Research Agency [Grants AMSE ANR-17-EURE-0020, Ecodec ANR-11-LABX-0047, and MLEforRisk ANR-21-CE26-0007] for supporting our research. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.02025 .

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Prediction of bubbles in presence of α-stable aggregates moving averages

Gilles de Truchis, Sébastien Fries, Arthur Thomas

Financial markets frequently exhibit dramatic episodes where asset prices undergo rapid growth followed by abrupt collapses, that are incompatible with standard linear time series models. While anticipative heavytailed linear processes offer a promising alternative for modeling such phenomena, they impose uniform bubble patterns across different episodes, contradicting empirical evidence. This paper introduces a new model, based on α-stable moving average aggregates, that accommodates heterogeneous bubble dynamics.

We establish the theoretical properties of this model, demonstrating that it admits a semi-norm representation on a unit cylinder, thereby enabling the prediction of extreme trajectories with varying growth dynamics. We develop a minimum distance estimation procedure based on the joint characteristic function and establish its asymptotic properties. Monte Carlo simulations confirm the estimator's good finite-sample performance across various specifications, and we implement a subsampling methodology to empirically verify the convergence to asymptotic normality. Our empirical application to the CBOE Crude Oil ETF Volatility Index successfully decomposes observed volatility dynamics into distinct components with different persistence properties, revealing that what appears as a single bubble episode actually consists of multiple superimposed processes with heterogeneous growth rates and crash probabilities.

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Trading Returns for Privacy: Experimental Evidence from Financial Data Leaks

Mehdi Louafi

This paper provides benchmark experimental evidence on how individuals trade off financial performance against the risk of disclosing their own financial information when privacy risks are explicit, and outcomes are immediate. In a laboratory investment task with repeated decisions, participants choose between two risky options that differ in expected return and in an explicitly stated probability that choosing one option triggers a pseudonymous disclosure of a limited subset of their pre-elicited financial information to other participants. Participants respond systematically to both return spreads and leak probabilities, but choices are substantially more elastic to financial incentives than to changes in leak risk. In dynamic analyses, experiencing an own leak has modest and short-lived effects, whereas higher leakage among other participants is followed by a lower propensity to select the privacy-risky option. Standard measures of risk and loss aversion and most economic characteristics explain little of the heterogeneity in choices, while context-relevant privacy attitudes are associated with more privacy-protective behavior. Overall, in this transparent-probability benchmark environment, monetary incentives dominate leak risk on average, while social information about others' leaks meaningfully shapes behavior.

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Tail Risk from Extreme Temperature in an Integrated Northeastern American Low- Carbon Electricity System

Anson T. Y. Ho, Adrien Desroziers, Kim P. Huynh, Marcel-Cristian Voia

Cross-border electricity trade in the Northeastern American grid helps U.S. markets mitigate structural deficits in low-carbon generation capacity. These deficits are partially offset by surplus exports from Québec (hydropower) and Ontario (nuclear generation). We use a Vine copula to assess the nonlinear interdependence of these markets.

Using hourly data from 2019-2023, we estimate the joint lower tail of regional net lowcarbon surpluses. In the unconditional distribution, the 5% Value-at-Risk (VaR) indicates an aggregate deficit of -1.3% of low-carbon generation. Regional VaR estimates reveal pronounced asymmetries as U.S. shortfalls exceed local low-carbon generation by about 130-178%. While Canadian regions are substantially less exposed in the lower tail. On heat-wave days, the aggregate deficit is -1.8% with the U.S. regional shortfalls about 200-250%. As heat waves increase in frequency and intensity, there will be an increase reliance on fossil fuels during electricity shortfalls.

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Fiscal Rules and Environmental Spending: Navigating the Trade- off between Discipline and Green Priorities

Ablam Estel Apeti, Bao We Wal Bambe, Jean-Louis Combes, Pascale Combes Motel, Rayangnewendé Frans Sawadogo

Environmental concerns are becoming more pressing as the climate emergency intensifies, posing a major challenge for many governments: increasing green investments to promote better adaptation and resilience to climate events, while maintaining fiscal discipline. This raises the question of whether governments that operate under fiscal rules tend to safeguard environmental spending in light of the climate emergency, or whether they are more inclined to scale it back to meet their fiscal targets, given that such investments require substantial public funding. Using data covering 31 advanced economies between 1995 and 2021, we find robust evidence that the strengthening of fiscal rules significantly reduces environmental spending, in particular debt rules and expenditure rules. Moreover, the adverse impact of fiscal rules on environmental expenditures is amplified during election periods, whereas it is mitigated in the presence of sound past fiscal conditions, the Kyoto Protocol, and stringent environmental policies. Further analysis reveals that although fiscal rules tend to reduce environmental spending, they are associated with greater efficiency in such spending.

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