Working Papers:
[NEW!] Systemic Growth-at-Risk and Growth Spread Measures [SSRN] [Slides]
with Abderrahim Taamouti
(in submission)
Abstract:
We develop a framework for measuring systemic growth risk across countries by quantifying how severe growth downturns in one economy are associated with the distribution of growth outcomes elsewhere. Extending the Growth-at-Risk literature to a multivariate setting, we introduce Systemic Growth-at-Risk measures that capture expected domestic growth losses conditional on distress abroad, together with Growth Spread measures that compare conditional growth outcomes under systemic expansions and contractions. To estimate these measures, we model the joint distribution of GDP growth for 18 European OECD economies using GARCH-based specifications combined with dynamic conditional correlations and copulas. Based on out-of-sample evaluations of marginal and joint predictive distributions, we select the Factor-GARCH-Copula specification for the main empirical analysis. Empirically, systemic growth losses increase sharply during major crises, particularly the COVID-19 pandemic, while bilateral exposures exhibit substantial heterogeneity across countries and episodes. The conditional growth spread narrows for the Eurozone cohort relative to non-Eurozone economies following the introduction of the euro. Finally, growth spreads are systematically associated with trade openness, public debt dynamics, and global uncertainty. Our findings reveal important heterogeneity in countries’ exposure to international growth risks and provide a new framework for analyzing systemic growth interdependence in integrated economies.
[NEW] Robust Estimation and Inference for Stock Return Predictability [Link]
with Hao Xu and Abderrahim Taamouti
(in submission)
Abstract:
We propose a sensitivity analysis approach to estimation and inference for stock return predictability with multiple persistent predictors and non-normal innovations. Unlike most of the existing literature, we allow the predictors’ residuals to be serially correlated, a case in which ordinary least squares is biased and the conventional T- and F-tests reject too often. We construct an artificial instrument following the IVX approach of Phillips and Magdalinos (2009) and embed it in a minimax sensitivity analysis framework that treats the degrees of endogeneity and persistence, the intercepts, and the serial correlation of the predictors’ residuals as locally misspecified. This delivers estimators and confidence intervals that remain valid in a neighbourhood of a reference model rather than at a single point. Monte Carlo simulations compare their coverage and length with those of ordinary least squares, the IVX test of Kostakis et al. (2015), and the Bonferroni interval of Campbell and Yogo (2006). An application to the S&P 500 index with the dividend-price and earnings-price ratios reassesses the robustness of predictability claims across subsamples and frequencies.
Portfolio Optimization with Growth-at-Risk: A Conditional Sharpe Ratio Approach [SSRN] [Slides]
with Abderrahim Taamouti
(in submission)
Abstract:
This paper studies portfolio choice under macroeconomic tail risk using the concept of Growth-at-Risk (GaR). We propose a conditional allocation framework that maximizes a Sharpe ratio evaluated in adverse macroeconomic states, thereby incorporating downside growth risk directly into portfolio decisions. The approach is implemented using forward-looking return scenarios generated by multivariate GARCH models, enabling a fully out-of-sample evaluation. Empirically, we consider U.S. sector ETFs and use the Chicago Fed National Activity Index (CFNAI) as a proxy for GaR. The results show that the resulting Conditional Sharpe Ratio (CoSR) portfolio delivers economically significant improvements in risk-adjusted performance relative to standard benchmarks. These gains remain robust after accounting for transaction costs, highlighting the value of conditioning portfolio allocation on macroeconomic downside risk..