Published in Working Paper, 2026
This paper introduces neural network-predicted factor loadings in the dynamic Nelson-Siegel yield curve model for simultaneous analysis and forecasting of interest rates across different maturities.
Recommended citation: Kooiker, S., van Brummelen, J., Schaumburg, J., & Zamojski, M. (2026). Dynamic Nelson Siegel Model with Time-Varying Neural Factor Loadings. Working Paper.
Published in Working Paper, 2025
A procedure that integrates an exponentially weighted moving average version of the Diks-Panchenko test with EWMA local density estimators to assess Granger causality in dynamic environments.
Recommended citation: Kooiker, S. (2025). Nonparametric Time-Varying Granger Causality. Working Paper.
Published in Working Paper, 2024
We propose to forecast multi-horizon Growth-at-Risk using flexible dynamic sequence models combining many-to-many recurrent neural networks with an objective function that guarantees non-crossing of quantile estimates.
Recommended citation: Kooiker, S., Hoesch, L., & Schaumburg, J. (2024). Multi-period Growth-at-Risk Forecasting with Recurrent Neural Network. Working Paper.