Department of Econometrics

The members of the department concentrated their research capacity on the following areas:

  • Econometrics
  • Deep Learning
  • Reinforcement Learning
  • Time series
  • Heterogeneous agents models
  • Macroeconomics
  • Spectral analysis

The Department of Econometrics focuses on advanced research in time series econometrics, macroeconomic modelling and forecasting. The team works on both theoretical and applied aspects of econometrics, often solving economic and financial problems using advanced statistical techniques such as deep machine learning. Specifically, we are active in the key research areas of financial econometrics, persistence of time series, asset pricing, energy economics, tail risks in financial markets, dynamic modelling of economic systems, agent-based models, as well as modern research areas of reinforcement learning solutions to decision problems and analysis of high-dimensional data sets (big data).

The department frequently collaborates with international institutions and contributes to the academic literature. Our research has appeared in Review of Economics and Statistics, Econometrics Journal, Journal of Financial Econometrics, Journal of Financial Markets, Econometric Reviews, Journal of Applied Econometrics, Journal of Economic Dynamics and Control, Energy Economics and The Energy Journal.

Our current projects

Understanding Household Responses to Inflation: Insights from Randomized Controlled Trials after High Inflation

GACR 25-17769S
2025-01-01 - 2027-12-31
Our research proposes three randomized controlled trials (RCTs) to examine the intricate relationship between inflation expectations, household behavior, and central bank credibility in the context of recent double-digit inflation in the Czech Republic. Our goal is to focus on the causal effects of the news about inflation and the communication of monetary policy on inflation expectations and household decisions. By conducting these experiments in an environment marked by recent inflationary pressures, our study will provide insights into central bank communication when the central bank seeks to restore its credibility and stabilize inflation expectations at the inflation target. Furthermore, we aim to uncover whether there is a systematic difference in the reactions of households depending on their liquidity constraints and wealth to enrich the growing literature pointing towards the importance of household heterogeneity on the transmission of monetary policy and the effects of policy shocks.

Taming the tail risks in financial markets with data-driven methods

GACR 24-11555S
2024-01-01 - 2026-12-31
The project will develop a new family of models for identification of tail risks in financial markets from possibly large datasets using deep learning algorithms. Our newly developed methods will allow us to revisit several classical problems in empirical asset pricing. We believe that the results will be of fundamental character and will open number of questions. Specifically, we aim to explore how deep learning and reinforcement learning can help us to understand the behavior of preference makers departing from classical rationality assumptions, especially those looking at quantile preferences and or heterogeneously persistent investment horizons.

Hedging uncertainty in commodity markets

GACR 24-115585
2024-01-01 - 2026-12-31
V rámci projektu budou navrženy a realizovány strategie zajištění na komoditních trzích zaměřené na snížení finančních rizik. Náš přístup zohlední rozdíly ve zpracování informací a investičních horizontech účastníků trhu využitím informačního obsahu realizovaných semikovariančních matic a frekvenčně specifického přenosu rizik napříč aktivy. Energetické komodity, jako je ropa a zemní plyn, budou předmětem zvláštního zájmu vzhledem k jejich důležitosti pro světovou ekonomiku. Věříme, že náš projekt přispěje k lepšímu pochopení dynamiky komoditních trhů a poskytne poznatky o efektivních zajišťovacích strategiích.

Deep dive into decentralized finance: Market microstructure, and behavioral and psychological patterns

GA24 23-06606S
2023-01-01 - 2025-12-31
Decentralized finance has been often synonymized with cryptocurrencies, cryptoassets, or even simply with Bitcoin not only in the public perception but to a high degree also in financial research. This project aims to dive deeper into decentralized finance and in a comprehensive manner explore and describe its structural aspects. We aim to answer how these features drive and influence dynamics and interconnections within the system, how liquidity is formed, and how the perceived dominance of retail/speculative investors projects into potential behavioral patterns, utilizing the unprecedented data availability decentralized finance offers. The project will focus on two main subtopics - market microstructure, and behavioral and psychological patterns - each with a set of specific research questions outlined in the proposal that will enrich the current understanding of these novel markets.

Contact

Secretariat: Dana Jeníkovská