Economists model tail risk and volatility amid macro uncertainty
Researchers and policymakers met at Banca d'Italia in Rome on September 24–25, 2026, to address macroeconomic modelling under uncertainty. Keynote lectures by Barbara Rossi and Dario Caldara led discussions on asymmetric risk, factor models and policy forecasting.
Keynotes tackle multipliers and volatility
Deputy Governor Chiara Scotti opened the two-day gathering at Palazzo Koch in Rome.
Keynote speaker Barbara Rossi of the European University Institute and Universitat Pompeu Fabra presented empirical methods for fiscal multipliers using time variation and high-frequency shocks.
Dario Caldara from the Federal Reserve Board addressed the joint dynamics of mean projections and volatility in risk modeling.
The five formal sessions focused on dynamic factor models under economic instability, macro-financial sentiment, growth-at-risk testing, asymmetric inflation skews, and machine learning for scenario forecasting.
A dedicated poster session examined topics including artificial intelligence uncertainty and euro area saving rates.
From pandemic scores to machine learning
The conference gathered economists from institutions including the Bank of Spain, the Federal Reserve Bank of San Francisco, the European Central Bank, and the Oesterreichische Nationalbank.
Research papers addressed practical technical challenges in macroeconomics, from recovering composite pandemic indices via score-driven dynamic factor models to estimating structural dynamic models with machine learning.
Presenters emphasized the limits of standard linear frameworks during major structural shifts.
Nonlinear reality breaks linear tools
Central banks must overcome standard linear models that faltered during recent supply shocks.
Integrating tail risks and asymmetric distributions is technically overdue, even if practical policy implementation remains slow.
Addressing non-normal volatility has shifted from an academic luxury to an operational necessity.