Research

Business Cycle and Realized Losses in the Consumer Credit Industry

2025, European Journal of Operational Research

joint with Walter Distaso (Imperial College Business School) & Frédéric Vrins (UCLouvain)

Abstract:

We investigate the determinants of losses given default (LGD) in consumer credit. Utilizing a unique dataset encompassing over 6 million observations of Italian consumer credit over a long time span, we find that macroeconomic and social (MS) variables significantly enhance the forecasting performance at both individual and portfolio levels, improving R² by up to 10 percentage points. Our findings are robust across various model specifications. Non-linear forecast combination schemes employing neural networks consistently rank among the top performers in terms of mean absolute error, RMSE, R2, and model confidence sets in every tested scenario. Notably, every model that belongs to the superior set systematically includes MS variables. The relationship between expected LGD and macro predictors, as revealed by accumulated local effects plots and Shapley values, supports the intuition that lower real activity, a rising cost-of-debt to GDP ratio, and heightened economic uncertainty are associated with higher LGD for consumer credit. Our results on the influence of MS variables complement and slightly differ from those of related papers. These discrepancies can be attributed to the comprehensive nature of our database – spanning broader dimensions in space, time, sectors, and types of consumer credit — the variety of models utilized, and the analyses conducted.

Evaluating Inflation Forecasts in the Euro Area and the Role of the ECB

2025, Journal of Forecasting

joint with Bertrand Candelon (UCLouvain)

Abstract:

This paper evaluates the informative value of the ECB inflation forecasts vis-à-vis other institutional and model-based forecasts in the euro area using ex-post optimal combinations of forecasts and nonnegative weights. From a methodological perspective, we adapt the corresponding forecast encompassing test to the constrained parameter space, showcasing its superior performance over traditional encompassing tests in both size and power properties. Empirically, the combining weights and the forecast encompassing test reveal that the ECB was the most informative forecaster of euro area inflation over the 2009-2021 period. This changed in 2022: the ECB lost its position as the most informative forecaster, and when using rolling windows to estimate the combining weights using a rolling window, we find an important decline in the ECB's weight over time. This time-dependency can be associated with the economic environment, and in particular the level of uncertainty, the monetary policy, and the macro-financial conditions, in which the ECB operates.

Optimal and Robust Combination of Forecasts via Constrained Optimization and Shrinkage

2022,  International Journal of Forecasting

joint with Paolo Gambetti (CRIF S.p.A.) &  Frédéric Vrins (UCLouvain)

For the correction of Proposition 1, please see:

Correction: Optimal and Robust Combination of Forecasts via Constrained Optimization and Shrinkage

2022,  International Journal of Forecasting, joint with Paolo Gambetti &  Frédéric Vrins

Abstract:

We introduce various methods that combine forecasts using constrained optimization with penalty. A non-negativity constraint is imposed on the weights, and several penalties are considered, taking the form of a divergence from a reference combination scheme. In contrast with most of the existing approaches, our framework performs forecast selection and combination in one step, allowing for potentially sparse combining schemes. Moreover, by exploiting the analogy between forecasts combination and portfolio optimization, we provide the analytical expression of the optimal penalty strength when penalizing with the L2-divergence from the equally-weighted scheme. An extensive simulation study and two empirical applications allow us to investigate the impact of the divergence function, the reference scheme, and the non-negativity constraint on the predictive performance. Our results suggest that the proposed models outperform those considered in previous studies.

Fragmentation in the European Monetary Union: Is it really over?

2022, Journal of International Money and Finance

joint with Bertrand Candelon (UCLouvain) & Angelo Luisi (UCLouvain)

Abstract:

Sovereign bond market fragmentation represents one of the major challenges European authorities have had to tackle since the outburst of the euro area debt crisis in 2010. By investigating the inter-country shock transmission through a new methodology that reconciles Factor and Global Vector Autoregressive models, we first show that fragmentation risk well preceded the sovereign debt crisis outburst. Most importantly, by analyzing the recent period, we document a rise in fragmentation risk in the euro area during the COVID pandemic. This rise, connected to the pressure on public debts and deficits due to the pandemic period, questions the European integration process and calls for early measures to avoid a new sovereign debt crisis.

Meta-learning approaches for recovery rate prediction

2022,  Risks

joint with Paolo Gambetti (CRIF S.p.A.) &  Frédéric Vrins (UCLouvain)

Abstract:

While previous academic research highlights the potential of machine learning and big data for predicting corporate bond recovery rates, the operations management challenge is to identify the relevant predictive variables and the appropriate model. In this paper, we use meta-learning to combine the predictions from 20 candidates of linear, nonlinear and rule-based algorithms, and we exploit a data set of predictors including security-specific factors, macro-financial indicators and measures of economic uncertainty. We find that the most promising approach consists of model combinations trained on security-specific characteristics and a limited number of well-identified, theoretically sound recovery rate determinants, including uncertainty measures. Our research provides useful indications for practitioners and regulators targeting more reliable risk measures in designing micro- and macro-prudential policies.