Anastasija Tetereva

Anastasija Tetereva is an Assistant Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. Her research lies at the intersection of machine learning and econometrics, with applications in financial markets, real estate pricing, and risk modeling.

Her research interests include Machine Learning, Econometrics, Volatility Forecasting, Hedonic Pricing, Financial Sentiment Analysis, and Granger Causality. She applies advanced computational methods to economic and financial data to improve forecasting and decision-making.

The recent articles reflect a strong trend in combining machine learning techniques—particularly random forests and hybrid models—with traditional econometric frameworks to analyze volatility, real estate valuations, and financial news causality. Her work demonstrates a consistent focus on improving predictive accuracy in financial and housing markets through data-driven modeling.

• A Forest Full of Risk Forecasts for Managing Volatility (2022)
• How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market (2022)
• Forecasting realized correlations: a MIDAS approach (2019)
• Sentiment spillover effects for US and European companies (2019)
• Application of Multivariate Hawkes Graphs to Uncover Granger Causality of Financial News (2018)

Anastasija Tetereva has actively contributed to the academic community, including co-organizing the event Financial Econometrics meets Machine Learning (FinEML) in 2023, indicating strong engagement in interdisciplinary research networks. While no direct mention of student advising or grant funding is made in the text, her publication record and collaborative work suggest involvement in research teams and potential supervision of junior researchers.

She is affiliated with the research network in financial econometrics and machine learning, contributing to a growing interdisciplinary community that bridges computer science and economic modeling. Her work is disseminated through working papers, peer-reviewed journals, and conference presentations, reflecting a robust and current research agenda.