Daniela Scidá is a manager and financial economist in the Quantitative Supervision and Research group of the Supervision, Regulation and Credit department. Scidá is currently based in Charlotte, North Carolina. Her research interests lie in the areas of time series analysis, financial econometrics, and financial networks. Scidá is particularly interested in applied econometrics and econometric methodology for time series. Her current research focuses on financial networks estimation and the intersection with Natural Language Processing (NLP) methods.

Scidá currently leads a supervisory project that leverages NLP methods and mapping techniques. Previously, she served as deputy lead of the pre-prevision net revenue modeling team of the Federal Reserve System Stress Testing program. Scida’s supervisory experience includes model development and forecasting under stress conditions, as well as examination from participation in the CCAR program. Until recently, she was also the co-coordinator of the QSR Seminars and Brown Bags series.

Before joining the Richmond Fed in 2016, Scidá completed her doctorate in economics at Brown University. She received a bachelor’s degree in economics from Universidad Nacional de Tucumán, Argentina, and a master’s degree in economics and finance from Centro de Estudios Monetarios y Financieros (CEMFI), Spain. Scidá is originally from Tucumán, a small province in the north of Argentina.