Daniela Scidá is a manager and financial economist in the Supervision, Regulation and Credit department. On the supervisory side, she leads an innovation project that leverages Natural Language Processing (NLP) methods and mapping techniques. In her previous role, she served as the Deputy Lead of the Pre-Prevision Net Revenue modeling team of the Federal Reserve System Stress Testing program. Her supervisory experience includes model development and forecasting under stress conditions as well as examination from participation in the Comprehensive Capital Analysis and Review program. Until recently, she was the co-coordinator of the Quantitative Supervision and Research unit’s Seminars and Brown Bags series.

Scidá, who is originally from Tucumán, a small province in northern Argentina, joined the Richmond Fed in 2016. Her research interests lie in the areas of time series analysis, financial econometrics and financial networks. She is particularly interested in applied econometrics and econometric methodology for time series. Many of her current research projects focus on financial networks estimation and, more recently, at the intersection with NLP methods.

She earned her bachelor’s degree from Universidad Nacional de Tucumán, a master’s degree from Centro de Estudios Monetarios y Financieros in Spain in addition to master’s and doctoral degrees in economics from Brown University.

View Scidá’s working papers on SSRN.