Sven Otto

Dr. Sven Otto is an Assistant Professor of Econometrics at the University of Cologne, Faculty of Management, Economics and Social Sciences, where he has been employed since 2023. His academic work focuses on advanced econometric methods, particularly in time series analysis and functional data. He is affiliated with the Institute of Econometrics and Statistics at the university.

His educational background includes:

• 2019: Dr. rer. pol., University of Cologne
• 2014: MSc in Economics, University of Bonn
• 2012: BSc in Mathematics and Economics, University of Bonn

Dr. Otto's research centers on developing and applying sophisticated econometric techniques to analyze complex time-dependent data. His work bridges theoretical econometrics with practical applications, particularly in financial and economic time series analysis. He has made significant contributions to the understanding of structural breaks, unit root testing, and functional data models.

His research has important implications for economic forecasting, financial risk assessment, and policy evaluation, especially in volatile periods such as the COVID-19 pandemic. Otto collaborates with researchers across institutions, including Jörg Breitung at the University of Cologne and Florian Stark.

Dr. Otto's recent publications demonstrate a clear trajectory toward increasingly sophisticated modeling of time-dependent phenomena, with a growing emphasis on functional data approaches and real-time monitoring techniques. His work shows a progression from theoretical foundations to practical applications, particularly in economic and financial contexts.

Current research projects include:

• Approximate Factor Models for Functional Time Series (with Narazii Salish)
• Functional Factor Regression with an Application to Electricity Price Curve Modeling (with Luis Winter)
• Combining Concurrent and Historical Functional Linear Regression (with Alois Kneip and Dominik Liebl)
• DFG Project No. 511905296 (2024-2026): Modeling Functional Time Series with Dynamic Factor Structures and Points of Impact