Department of Econometrics focuses on understanding and modelling important economic and financial problems like decision making of agents, asset pricing, understating interaction between agents, or recently understanding the economic impacts of pandemics. We offer solutions to these problems with the help of mathematical models as well as statistical methodologies. More recently, we utilize modern machine learning methods for decision-making problems and analyze high-dimensional data sets (big data). We particularly focus on understanding economic and financial problems, focusing on estimation of real-world data.

In our department, we cover topics such as Machine Learning/Statistical Learning, Dynamic networks and financial decision making, Dynamic quantile asset pricing models, Measurement of dependence between cyclical economic variables, High-frequency data analysis, Agent based models, Stochastic optimization and Macroeconomics.

Working Papers:

• Uncertainty Network Risk and Currency Returns. M. Babiak, J. Barunik. preprint draft (Jan 2021).
  
• Estimation of heuristic switching in behavioral macroeconomic models. J. Kukacka, S. Sacht. Kiel University Economics Working Paper (2021)
• Dynamic industry uncertainty networks and the business cycle. J. Barunik, M. Bevilacqua and R. Faff. preprint draft (Jan 2021).
• Dynamic Networks in Large Financial and Economic Systems. J Barunik, M. Ellington. preprint draft (2021).
• Do Rural Banks Matter That Much? Burgess and Pande (2005) Reconsidered. N. Buliskeria, J. Baxa. preprint (2020).
• Dynamic Network Risk J. Barunik, M. Ellington. submitted (2020). 
• Does calibration affect the complexity of agent-based models? A multifractal grid analysis. J. Kukacka, L. Kristoufek. preprint (2020)
• Deep Learning, Predictability, and Optimal Portfolio Returns. J Barunik, M. Babiak. preprint draft (Sept 2020).

Selected Papers:

• Asymmetric Network Connectedness of Fears. J. Barunik, M. Bevilacqua,
  R. Tunaru. The Review of Economics and Statistics (forthcoming).
• Measurement of Common Risks in Tails: A Panel Quantile Regression Model for Financial Returns. J. Barunik, F. Cech. Journal of Financial Markets (forthcoming).
• Multi-stage emissions management of a steel company. F. Zapletal, M. Šmíd, M. Kopa. Annals of Operations Research, 292, 2020, pp. 735–751.
• Forecasting dynamic return distributions based on ordered binary choice. S. Anatolyev, J. Barunik. International Journal of Forecasting , 35(3), 2019, pp. 823-835.
• Quantile coherency: A general measure for dependence between cyclical economic variables. J. Barunik, T. Kley. Econometrics Journal, 22(2), 2019, pp. 131-152.
• What type of finance matters for growth? Bayesian model averaging evidence. H. Iftekhar, R. Horvath, and J. Mares. The World Bank Economic Review, 32(2), 2018, pp. 383-409.
• Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk. J. Barunik, T. Krehlik. Journal of Financial Econometrics, 16(2), 2018, pp. 271-296.
• Do co-jumps impact correlations in currency markets? J. Barunik, L. Vacha. Journal of Financial Markets, 37(1), 2018, p. 97-119.
• Estimation of financial agent-based models with simulated maximum likelihood. J. Kukacka, J. Barunik. Journal of Economic Dynamics and Control, 85, 2017, pp. 21-45.
• Modeling and Forecasting Persistent Financial Durations. F. Zikes, J. Barunik, N. Shenai. Econometric Reviews, 36(10), pp. 1081-1110.
• Asymmetric volatility connectedness on the forex market. J. Barunik, E. Kočenda, L. Vacha. Journal of International Money and Finance, 77, 2017, pp. 39-56.
• Modeling and forecasting exchange rate volatility in time-frequency domain. J. Barunik, T. Krehlik, L. Vacha. European Journal of Operational Research, 251(1), 2016, pp. 329-340.
• Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers. J. Barunik, E. Kočenda, L. Vacha. Journal of Financial Markets, 27, 2016, pp. 55–78.
• Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility. F., Žikeš, J. Baruník. Journal of Financial Econometrics. 14 (1), 2016, pp. 185-226.

Organized conferences and workshops:

• Haindorf workshop 2022, 2020, 2019, 2018, 2017, 2016. The series of joint workshops with Humboldt University organized in January are focused on networking activities of research groups of prof. Barunik and prof.Hardle and training PhD students in statistical techniques. We have enjoyed hosting several respected scholars who joined the workshops including Victor Chernozhukov (MIT), Oliver Linton (Cambridge), Bryan Graham (UC Berkeley), Qiwei Yao (LSE), Holger Dette (Bochum) and many visiting international scholars.
• STAT of ML 2021, 2020, 2019, Prague. Economterics Department in cooperation with Humboldt-Universität zu Berlin and Faculty of Mathematics and Physics, Charles University in Prague organized a STAT of ML (Statistics of Machine Learning) conference held October 7-8, 2021.  
• 2015 – 2020 Research Seminar Series – Jointly with Institute of Economic Studies we organize occasional small workshops for PhD students with invited international speakers, i.e. Eddie Gerba (LSE), Mattia Bevilaqua (LSE), Todorova (Bocconi).
• FinMaP – Financial Distortions & Macroeconomic Performance 2015 Prague: 2nd general workshop of the consortium organized by Institute of Information Theory and Automation.
• FinMaP – Financial Distortions & Macroeconomic Performance 2015 Mannheim: 3rd general workshop of the consortium co-organized by Institute of Information Theory and Automation.
• 2015 Econophysics Colloquium: The 2015 annual meeting of international researchers that brings together interdisciplinary research as physicists, economists and practitioners to discuss statistical methods, quantitative measures, modelling, simulations, and computational issues was co-organized in Prague by Institute of Information Theory and Automation and Charles University.