Research
Research
Deep Learning Based Residuals in Non-linear Factor Models: Precision Matrix Estimation of Returns with Low Signal-to-Noise Ratio, joint with Mehmet Caner,
Journal of Econometrics, 2025, forthcoming
Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices, joint with Winfried Pohlmeier and Aygul Zagidullina,
Journal of Financial Econometrics, 2024, 23(1)
Selecting the Number of Factors in Approximate Factor Models using Group Variable Regularization,
Econometric Reviews, 2024, 43(10), 796–823
An observation-driven mixed-frequency VAR model with closed-form solution, joint with Heiner Mikosch and Stefan Neuwirth, current version
Targeted Transformations for Macroeconomic Forecasting, joint with Philipp Kronenberg and Tim Reinicke, current version
A regularized structural factor-augmented vector autoregressive model, joint with Julie Schnaitmann, current version
Deep learning Sharpe Ratio, joint with Mehmet Caner
Optimal predictor and transformation selection for macroeconomic forecasting using variable importance in random forests, joint with Tim Reinicke