About

Jozef Baruník is a professor at the Institute of Economic Studies, Charles University in Prague, where he is the Director of the Master in Finance and Data Analytics (MFDA) programme. He is also head of the Econometrics Department at the Czech Academy of Sciences. In his research he develops mathematical models for understanding financial problems (such as the measurement and management of financial risk), develops statistical methods and analyses financial data. He is particularly interested in asset pricing, high-frequency data, financial econometrics, machine learning, high-dimensional financial datasets (big data), and frequency domain econometrics (cyclical properties and behaviour of economic variables).

In addition to his academic roles, Jozef has contributed to the practical application of his research, and often takes on a variety of roles such as panel member for ERC consolidator, an external consultant for the Market Intelligence division of the Bank of England or other funding agencies and professional bodies.

Jozef’s research has appeared in the Review of Economics and Statistics, Econometrics Journal, Journal of Financial Econometrics, Journal of Financial Markets, Econometric Reviews, Journal of Economic Dynamics and Control, The Energy Journal, he is an associate editor of the Digital Finance, Journal of Economic Interaction and Coordination, and Kybernetika.

Commonalities in Firm-level Implied Volatilities (with M.Babiak, M.Ellington, and M.Bevilacqua) preprint draft on SSRN (Nov 2024)
R&R, Journal of Financial and Quantitative Analysis

Repo dealer-driven bond mispricing (with C. Salazar, E. Gerba) NEW PAPER!
Bank of England Staff Working Paper No. 1,145 (Oct 2025)

Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks (with M.Nevrla) preprint draft (Aug 2025 - updated version available)
2nd R&R, Journal of Financial and Quantitative Analysis

Forecasting stock return distributions around the globe with quantile neural networks (with M.Hronec and O.Tobek) (August 2025)
2nd R&R, International Journal of Forecasting

Deep Learning, Predictability, and Optimal Portfolio Returns (with M.Babiak)
preprint draft (July 2021)
R&R, Journal of Empirical Finance

The Dynamic Persistence of Economic Shocks (with L.Vacha)
forthcoming, Review of Economics and Statistics preprint draft (June 2025 - updated version available)
replication code and package in Julia (June 2025 - updated version available)

Learning the Probability Distributions of Day-Ahead Electricity Prices (with L.Hanus)
Energy Economics (2025) Vol. 152, 108988
replication code and package in Julia

Predicting the volatility of major energy commodity prices: the dynamic persistence model (with L.Vacha)
Energy Economics (2024), Vol. 140, 107982
replication code and package in Julia

Dynamic industry uncertainty networks and the business cycle (with M.Bevilacqua and R.Faff)
Journal of Economic Dynamics & Control (2024), Vol. 1598, 104793

Persistence in Financial Connectedness and Systemic Risk (with M.Ellington)
European Journal of Operational Research (2024), forthcoming
code and package in Julia and Matlab

Quantile Spectral Beta: A Tale of Tail Risks, Investment Horizons, and Asset Prices (with M.Nevrla)
Journal of Financial Econometrics (2023), Vol. 21, No. 5, pp. 1590–1646
replication codes

Asymmetric Network Connectedness of Fears (with M.Bevilacqua, and R.Tunaru)
The Review of Economics and Statistics (2022), 104(6): 1-13
replication codes and data

Measurement of Common Risks in Tails: A Panel Quantile Regression Model for Financial Returns (with F.Cech)
Journal of Financial Markets (2021), 52, 100562,
replication codes and data

Forecasting dynamic return distributions based on ordered binary choice (with S.Anatolyev)
International Journal of Forecasting (2019), 35(3), pp.823-835,
code and package

Quantile Coherency: A General Measure for Dependence between Cyclical Economic Variables (with T.Kley)
The Econometrics Journal (2019), 22(2), pp. 131-152,
code and package
Supplementary material available

Total, asymmetric and frequency connectedness between oil and forex markets (with E. Kocenda)
The Energy Journal (2019),vol 40, pp. 157 - 174,
codes for introduced measures
Note the package frequencyConnectedness available here can be used to replicate the paper
Supplementary material available

Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk (with T.Krehlik)
Journal of Financial Econometrics (2018), 16 (2), pp. 271–296,
code and package
Supplementary material available

Do co-jumps impact correlations in currency markets? (with L.Vacha)
Journal of Financial Markets (2018), 37, pp.97-119,
codes for introduced measures

Modeling and Forecasting Persistent Financial Durations (with F.Zikes and N.Shenai)
Econometric Reviews (2017), 36:10, 1081-1110,
Supplementary material available

Estimation of financial agent-based models with simulated maximum likelihood (with J.Kukacka)
Journal of Economic Dynamics and Control (2017), 85, pp. 21-45,

Asymmetric volatility connectedness on forex markets (with E. Kocenda and L.Vacha)
Journal of International Money and Finance (2017), 77C, pp. 39-56,
code for replication

Cyclical properties of supply-side and demand-side shocks in oil-based commodity markets (with T.Krehlik)
Energy Economics (2017), 65, pp.208-218,
code and package codes for bootstrap

Good volatility, bad volatility: Which drives the asymmetric connectedness of Australian electricity markets? (with N. Apergis and M.Lau)
Energy Economics (2017), 66, pp.108- 115,
codes for introduced measures
Note the package frequencyConnectedness available here can be used to replicate the paper

Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers (with E. Kocenda and L.Vacha)
Journal of Financial Markets (2016), 27, 55–78,
codes for introduced measures
Note the package frequencyConnectedness available here can be used to replicate the paper

Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility (with F.Zikes)
Journal of Financial Econometrics (2016), 14 (1), 185–226,
Supplementary material available

Forecasting the term structure of crude oil futures prices with neural networks (with B.Malinska)
Applied Energy (2016), 164, pp.366–379,

Modeling and forecasting exchange rate volatility in time-frequency domain (with T.Krehlik and L.Vacha)
European Journal of Operational Research (2016), 251 (1), pp. 329–340,
codes to paper

Volatility spillovers across petroleum markets (with E. Kocenda and L.Vacha)
The Energy Journal (2015), 36(3), 309-329
codes for introduced measures
Note the package frequencyConnectedness available here can be used to replicate the paper

Are benefits from oil - stocks diversification gone? A new evidence from a dynamic copulas and high frequency data (with K.Avdulaj)
Energy Economics (2015), 51, pp. 31-44,

Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility (with J.Kukacka)
Quantitative Finance (2015), 15 (8), pp. 1347-1364,

Realized wavelet-based estimation of integrated variance and jumps in the presence of noise (with L.Vacha)
Quantitative Finance (2015), 15 (6), pp. 959-973,
codes for introduced measures

Are Bayesian Fan Charts Useful? The Effect of Zero Lower Bound and Evaluation of Financial Stability Stress Tests (with M.Franta, R.Horvath,K.Smidkova)
International Journal of Central Banking (2014), 10(1), 159-187

Comovement of energy commodities revisited: Evidence from wavelet coherence analysis (with L.Vacha)
Energy Economics (2012), 34(1), pp. 241–247,

Can a stochastic cusp catastrophe model explain stock market crashes? (with M.Vosvrda)
Journal of Economic Dynamics and Control (2012), 33(10), 1824-1836

Full list of publications available here

Tailoring Portfolio Choice via Quantile-Targeted Policies (with L.Janasek, A.Sarkany) NEW PAPER!
preprint (Oct 2025)

Abstract: We study the dynamic investment decisions of investors who prioritise specific quantiles of outcomes over their expected values. Downside-focused agents targeting low quantiles reduce risk in states with high variance, while those with a preference for high quantiles concentrate in sleeves with high dispersion when there is potential for upside. These results provide a microfoundation for volatility management, demonstrating that reducing exposure in volatile states is an optimal response for risk-averse investors and rationalising inverse-variance heuristics. We propose a distributional actor-critic algorithm that learns time-consistent policies tailored to these specific risks, irrespective of the utility’s functional form. The quantile value can be mapped onto interpretable tilts, and the performance of empirically chosen portfolios aligns with investors’ objectives.

“Who’s the Boss?”” The Role of Shareholders in Banks’ Lending Decisions (with P.Katsoulis, E.Gerba and J.A. Smith)
preprint draft (Oct 2024)

Abstract: We investigate banks’ lending decisions in the face of shareholder pressure to make payouts. We exploit the regulatory ban on banks’ distributions during the Covid-19 pandemic to show that the conflict between the interests of banks’ shareholders and their borrowers can persist even with such a ban. First, we find that restricted banks faced an increase in shareholders’ required rate of return, which was even higher for the banks most exposed to income-oriented investors. This led to an increase in the required rate of return on their overall capital. Second, banks responded by increasing lending volumes only to medium-sized and larger firms and on smaller loans, which are less capital intensive. However, they utilised government guarantee schemes to increase lending volumes to smaller firms and on larger loans. The results suggest that distribution restrictions can incentivise banks to increase lending, but in a way that minimises the capital impact to appease shareholders.

The Common Factor in Volatility Risk Premia (with M.Babiak, M.Ellington, and M.Bevilacqua)
preprint draft (Feb 2024)

Abstract: We show that firm-level volatility risk premium obeys a strong factor structure. Such factor structure is also present in a decomposition of firms’ volatility risk premium into good and bad counterparts, capturing compensation for realized volatility in positive and negative returns. Stocks with the weakest exposures to the common bad volatility risk premium factor earn average returns 7.32% higher than those with the strongest exposures. The common factor in total (bad) volatility risk premium predicts stock market returns at all horizons up to 24 months (from 6 months) both in-sample and out-of-sample. This predictive power is incremental to existing predictors.

Volatility Shocks and Currency Returns (with M.Babiak)
preprint draft (July 2025)

Presented at at 2023 WFA (San Francisco), 2023 SGF Conference (Zurich), 2023 EEA-ESEM (Barcelona)

Abstract: This paper examines how shocks to currency volatilities predict exchange rates. Theoretically, we develop a multi-country model where jumps in one country can influence others. We demonstrate that a currency’s expected excess return and volatility depend on the jump effects across all countries. Empirically, we document a negative cross-sectional association between the currency premia and transmitted volatility shocks to others, consistent with the theory. Buying the weakest and selling the strongest transmitters of strongly correlated volatility shocks resembles the carry trade. In contrast, a strategy based on volatility shocks that control for common correlation reflects a novel source of predictability.

The video below illustrates the short-term currency network during the Global Financial Crisis. If the video does not play properly, feel free to watch it here.

Current (Full-Time) Doctoral Students

Attila Sarkany: Machine Learning in Finance

Lukas Janasek: Quantile Deep Reinforcement Learning in Economics

Lenka Nechvatalova: Deep Reinforcement Learning and Portfolio Management

Past Doctoral Students

Josef Kurka: Horizon-specific risks, higher moments, and asset prices

Martin Hronec: Asset Pricing with Quantile Machine Learning

Matej Nevrla: Tail risks, asset prices, and investment horizons

Lubos Hanus (joint supervision with L.Vacha) Dynamic density forecasting using machine learning

Krenar Avdulaj: Essays in Financial Econometrics

Frantisek Cech: Three Essays on Risk Modelling and Empirical Asset Pricing

Tomas Krehlik: Applications of Modern Spectral Tools in Financial Econometrics

Jiri Kukacka: Estimation of Financial Agent-Based Models

Barbora Gregor: Three Essays on Data-Driven Methods in Asset Pricing and Forecasting

JEM217 - Advanced Econometrics (Masters)

The objective of the course is to help students understand several important modern techniques in econometrics and apply them in empirical research and practical applications. Emphasis of the course will be placed on understanding the essentials underlying the core techniques, and developing the ability to relate the methods to important issues faced by a practicioner.

By completing this course, students will be able to use a computer based statistical software to analyze the data, choose appropriate models and estimators for given economic application, understand and interpret the results in detail (diagnose problems, understand proper inference) and will be confident to carry out the analysis and conclusions with respect to appropriatness and limitation of the methodology used. Finally, students will have sufficient grounding in econometric theory to begin advanced work in the field.

JEM059,061 - Financial Econometrics I and II (Masters)

The objective of the course is to introduce advanced time series methods. Students will be able to use the modern financial econometric tools after passing this course and will be prepared to continue in the Quantitative Finance II course. Part of the course is also focused on the high frequency data econometrics.

JEM116 - Applied Econometrics (Masters)

The course concentrates on the practical use of econometric methods, reviewing the relevant methodology, its use, and the possible alternative modeling approaches. The lectures are supplemented by computer classes, where students can gain hands-on experience in applied econometric analysis. During the course we will especially focus on time series techniques applied to forecasting asset volatility, modeling inflation, exchange rate volatility and other topics that you may regularly encounter in economic and financial literature. The course focuses on following topics in econometrics: OLS, IV, ARIMA, GARCH, VAR, cointegration, non-linear model and limited dependent variable. The course is especially suited for students undertaking empirical exercise in writing their M.A. thesis.

JED412,413 - Advanced Financial Econometrics (PhD seminar)

The aim of this seminar is an analysis of macroeconomic systems primarily by methods of nonlinear dynamics, stochastics dynamics, and nonlinear time series analysis. Special application will be focused on a behavior of the financial markets.

doc. PhDr. Jozef Baruník, Ph.D.

Associate Professor
Department of Macroeconomics and Econometrics
Institute of Economic Studies, Faculty of Social Sciences
Charles University
Opletalova 26
Prague 1, 110 00,Czech Republic

barunik [AT] fsv [DOT] cuni [DOT] cz
00 (420) 776 259273
https://ies.fsv.cuni.cz/en/contacts/people/29200599

Head of the Department
Department of Econometrics
Institute of Information Theory and Automation
Academy of Sciences of the Czech Republic
Pod Vodarenskou Vezi 4
Prague 8, 182 08,Czech Republic

barunik [AT] utia [DOT] cas [DOT] cz
00 (420) 776 259273
https://utia.cas.cz/en/people/?pid=906