Publications:
Portfolio Selection under Systemic Risk [Link]
with Jose Olmo and Abderrahim Taamouti (2025)
Journal of Money, Credit and Banking (ABS 4)
Portfolio Selection under Non-Gaussianity and Systemic Risk: A Machine Learning Based Forecasting Approach [Link]
with Abderrahim Taamouti (2024)
International Journal of Forecasting (ABS 3)
Working Papers:
[NEW 2026] Systemic Growth-at-Risk and Growth Spread Measures [SSRN Link]
with Abderrahim Taamouti
(Under Review)
Abstract:
This paper develops two forward‑looking macroeconomic metrics – Systemic Growth-at-Risk (GaR) Measures and Growth Spread Measures – to quantify cross‑country growth risks and net gains from regional economic integration. Systemic GaR extends the standard GaR to a multi‑country setting by capturing a country’s growth shortfall during severe downturns elsewhere. Growth Spread Measures compare expected gains in union‑wide expansions with expected losses in union‑wide recessions. To estimate these measures, we model the joint GDP growth distributions for EU countries using GARCH models with copula and DCC dynamics, with results favoring a factor‑based GARCH-Copula specification emphasizing common shocks and tail dependence. Empirically, net growth dividends vary: countries with stronger fiscal positions, greater trade openness, and lower exposure to global uncertainty capture larger gains and exhibit resilience. Core northern economies face stronger downside exposure to peripheral recessions, underscoring the dual role of economic unions as sources of shared growth and systemic risk.
Enhancing Portfolio Resilience to Systemic Risk: A Neural Network Approach [SSRN Link]
with Abderrahim Taamouti (2025)
(Revise & Resubmit to Journal of Empirical Finance)
Abstract:
This paper aims to enhance the classical mean-variance portfolio selection model by using machine learning techniques and accounting for systemic risk. The optimal portfolio is obtained through a three-step supervised learning model. Firstly, the Smooth Pinball Neural Network is employed to predict return distributions of individual assets and the market. Secondly, we use a copula to model dependence between assets and the market, based on which we simulate return scenarios. Lastly, we maximize an ex-ante Sharpe ratio conditioning on systemic events. We train our models using a comprehensive dataset of nearly 600 U.S. individual stocks over 37 years; however, the portfolio analysis and backtesting are conducted on several representative portfolio sets with sizes up to 50 assets. This design allows the models to learn from a broad cross-section of firms while focusing the evaluation on computationally tractable portfolio dimensions. Our set of predictors includes 94 firm characteristics, 14 macroeconomic variables, and 74 industry dummies. The backtesting results demonstrate the superiority of our proposed approach over popular benchmark strategies including a GARCH-based model. This outperformance is statistically significant and robust to the inclusion of transaction costs.
Work in Progress:
Robust Estimation and Inference for Stock Return Predictability
with Hao Xu and Abderrahim Taamouti