Non-parametric volatility modeling in macroeconomics and finance.

Summary. During recessionary episodes (such as the global financial crisis or the Covid-19 pandemic) many important macroeconomic and financial quantities experience substantial shifts in the volatility process, while during tranquil periods the shock volatilities remain relatively stable. This project sets forth a research agenda to develop and apply more flexible volatility models for macroeconomic and financial data. These methods will be also useful for practitioners working on emerging market economies. These countries often feature short time series which are subject to several structural breaks. The methods we propose in this project are exactly designed for such situations where time series exhibit substantial shifts in the variance. We will illustrate the models developed in this project through various now- and forecasting applications. Apart from focusing on predictions, our models can be used to produce timely indicators of macroeconomic stability. These indicators can be applied to emerging market economies and implemented such that policy institutions have alternative high frequency measures of the overall state of a given economy.

Published papers produced as part of this project.

• Bayesian modeling of time-varying parameter vector autoregressions using regression trees (Annals of Applied Statistics, 2026).
• Bayesian neural networks for macroeconomic analysis (Journal of Econometrics, 2025).
• Gaussian process VARs and macroeconomic uncertainty (Journal of Business & Economic Statistics, 2025).
• High-frequency and heteroskedasticity identification in multicountry models: Revisiting spillovers of monetary shocks (Macroeconomic Dynamics, 2025).
• Machine learning the macroeconomic effects of financial shocks (Economics Letters, 2025).
• Predictive density combination using Bayesian machine learning (International Economic Review, 2025).
• Sparse time-varying parameter VECMs with an application to modeling electricity prices (International Journal of Forecasting, 2025).
• Dynamic shrinkage priors for large TVP regressions using scalable MCMC methods (Studies in Nonlinear Dynamics & Econometrics, 2024).

Working papers completed as part of this project.

• A Bayesian Gaussian process dynamic factor model.
• Bayesian modelling of VAR precision matrices using stochastic block networks.
• Coarsened Bayesian VARs: Correcting BVARs for incorrect specification.
• Direct Gaussian process predictive regressions with mixed frequency data.
• Monetary policy and the joint distribution of income and wealth: The heterogeneous case of the euro area.

Presentations by project members at international conferences for disseminating results.

• Robust Bayesian VARs through power posteriors presented by Tobias Scheckel at the 16th RCEA Bayesian Econometrics Workshop 2026 (Madrid).
• Stochastic block network vector autoregressions presented by Tobias Scheckel at CFE 2023.
• The international effects of central bank information shocks presented by Anna Stelzer at ICMAIF 2023.
• Nonparametric methods for measuring asymmetries in monetary policy transmission presented by Anna Stelzer at CFE 2022.

Posters presented by project members at international conferences.

• A Bayesian Gaussian process dynamic factor model
• When risk rises: Geopolitical risk and the transmission of global shocks

Final project workshop (PhD/ECR workshop, October 2026). The workshop agenda is available here. Presentations related to the project:

• Bottom-up Bayesian predictive synthesis for euro area inflation
• Robust Bayesian VARs through power posteriors
• When risk rises: Geopolitical risk and the transmission of global shocks