Jozef Barunik


Jozef Baruník is an Associate Professor at the Institute of Economic Studies, Charles University in Prague. He also serves as a head of the Econometrics department at the Czech Academy of Sciences. In his research, he develops mathematical models for understanding financial problems (such as measuring and managing financial risk), develops statistical methods and analyzes financial data. Especially, he is interested in asset pricing, high-frequency data, financial econometrics, machine learning, high-dimensional financial data sets (big data), and frequency domain econometrics (cyclical properties and behavior of economic variables).

Jozef’s work 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, and Journal of Economic Interaction and Coordination and also referees frequently for several journals and grant agencies in the fields of econometrics, finance, and statistics.

Selected Publications

Asymmetric Network Connectedness of Fears (with M.Bevilacqua, and R.Tunaru)
The Review of Economics and Statistics (2020) forthcoming

Measurement of Common Risks in Tails: A Panel Quantile Regression Model for Financial Returns (with F.Cech)
Journal of Financial Markets (2020) forthcoming

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),forthcoming
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

Working Papers

Frequency-Dependent Higher Moment Risks (with J.Kurka)
preprint draft (April 2021)

Abstract: Based on intraday data for a large cross-section of individual stocks and exchange traded funds, we show that short-term as well as long-term fluctuations of realized market and average idiosyncratic higher moments risks are priced in the cross-section of asset returns. Specifically, we find that market and average idiosyncratic volatility and kurtosis are significantly priced by investors mainly in the long-run even if controlled by market moments and other factors, while skewness is mostly short-run phenomenon. A conditional pricing model capturing the time-variation of moments confirms downward-sloping term structure of skewness risk and upward-sloping term structure of kurtosis risk, moreover the term structures connected to market skewness risk and average idiosyncratic skewness risk exhibit different dymanics.

Currency Network Risk (with M.Babiak)
preprint draft (July 2021)

Abstract: This paper identifies new currency risk stemming from a network of idiosyncratic option-based currency volatilities and shows how such network risk is priced in the cross-section of currency returns. A portfolio that buys net-receivers and sells net-transmitters of short-term linkages between currency volatilities generates a significant Sharpe ratio. The network strategy formed on causal connections is uncorrelated with popular benchmarks and generates a significant alpha, while network returns formed on aggregate connections, which are driven by a strong correlation component, are partially subsumed by standard factors. Long-term linkages are priced less, indicating a downward-sloping term structure of network risk.

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.

Dynamic industry uncertainty networks and the business cycle (with M.Bevilacqua and R.Faff)
preprint draft (March 2021)

Abstract: We argue that uncertainty network structures extracted from option prices contain valuable information for business cycles. Classifying U.S. industries according to their contribution to system-related uncertainty across business cycles, we uncover an uncertainty hub role for the communications, industrials and information technology sectors, while shocks to materials, real estate and utilities do not create strong linkages in the network. Moreover, we find that this ex-ante network of uncertainty is a useful predictor of business cycles, especially when it is based on uncertainty hubs. The industry uncertainty network behaves counter-cyclically in that a tighter network tends to associate with future business cycle contractions.

Deep Learning, Predictability, and Optimal Portfolio Returns (with M.Babiak)
preprint draft (July 2021) available

Abstract: We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty equivalent returns and Sharpe ratios. We demonstrate that a long-short-term-memory recurrent neural network, which excels in learning complex time-series dependencies, generates a superior performance among a variety of networks considered. Return predictability via deep learning generates substantially improved portfolio performance across different subsamples, particularly during recessionary periods. These gains are robust to including transaction costs, short-selling and borrowing constraints.

Dynamic Networks in Large Financial and Economic Systems (with M.Ellington)
revised (2021)

Abstract: This paper identifies frequency-dependent network structures that evolve over time. To measure such dynamic networks, we propose a computationally efficient framework that is widely applicable to many economic and financial datasets, and readily available for high dimensional models. We provide Monte Carlo evidence that our measures are able to reliably recover true network connections from a battery of DGPs and also develop a testing procedure for statistical differences among frequency-dependent network connections. Our empirical application on firm-level realized volatilities documents substantial heterogeneities in dynamic network structures that may be useful as an online monitoring tool to help guide macro-prudential policy.
code and package in Julia and Matlab

Dynamic Network Risk (with M.Ellington)
submitted (2020)

Abstract: This paper examines the pricing of short-term and long-term dynamic network risk in the cross-section of stock returns. Stocks with high sensitivities to dynamic network risk earn lower returns. We rationalize our finding with economic theory that allows the stochastic discount factor to load on network risk through the precautionary savings channel. A one-standard deviation increase in long-term (short-term) network risk loadings associate with a 7.66% (6.71%) drop in annualized expected returns.
code and package in Julia and Matlab

Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual Tool for Economists (with Cathy Yi-Hsuan Chen, and J.Vecer)
Submitted (2019)

Abstract: We propose how to quantify high-frequency market sentiment using high-frequency news from NASDAQ news platform and support vector machine classifiers. News arrive at markets randomly and the resulting news sentiment behaves like a stochastic process. To characterize the joint evolution of sentiment, price, and volatility, we introduce a unified continuous-time sentiment-driven stochastic volatility model. We provide closed-form formulas for moments of the volatility and news sentiment processes and study the news impact. Further, we implement a simulation-based method to calibrate the parameters. Empirically, we document that news sentiment raises the threshold of volatility reversion, sustaining high market volatility.

codes to paper

Tail Risks, Investment Horizons, and Asset Prices (with M.Nevrla)
Submitted (2020)

Abstract: We show that the two important sources of risk – market tail risk and extreme market volatility risk – are priced in the cross-section of asset returns heterogeneously across horizons. Specifically, we find that tail risk is a short-term phenomenon whereas extreme volatility risk is priced by investors in the long-term. These risks stem from a dependence structures in the joint distribution of stochastic discount factor and asset returns at various investment horizons that are more general than usually assumed by traditional covariance-based measures. The risk premium we document suggests that investors care about the transitory as well as persistent shocks.

Co-jumping of Treasury Yield Curve Rates (with P.Fiser)
Submitted (2019)

Abstract: We study the role of co-jumps in the interest rate futures markets. To disentangle continuous part of quadratic covariation from co-jumps, we localize the co-jumps precisely through wavelet coefficients and identify statistically significant ones. Using high frequency data about U.S. and European yield curves we quantify the effect of co-jumps on their correlation structure. Empirical findings reveal much stronger co-jumping behavior of the U.S. yield curves in comparison to the European one. Further, we connect co-jumping behavior to the monetary policy announcements, and study effect of 103 FOMC and 119 ECB announcements on the identified co-jumps during the period from January 2007 to December 2017.

Codes for introduced measures

Investment Disputes and Abnormal Volatility of Stocks (with Z.Drabek and M.Nevrla)
Submitted (2020)

Abstract: Dramatic growth of investment disputes between foreign investors and host states rises serious questions about the impact of those disputes on investors. This paper is the first to explain increased uncertainty of investors about the outcome of arbitration, which may or may not lead to compensation for damages claimed by the investor. We find robust evidence that investment disputes lead to abnormal share fluctuations of companies involved in disputes with host countries. Importantly, while a positive outcome for an investor decreases uncertainty back to original levels, we document strong increase in the volatility of companies with negative outcome for the investor. We find that several variables including size of the award, political instability, location of arbitration, country of origin of investor or public policy considerations in host country explain large portion of the investor’s uncertainty.

Work in Progress

Deep Reinforcement Learning for Dynamic Decision Making with Quantile Preferences (with L.Vacha)

Asset Pricing with Quantile Machine Learning (with M.Hronec)

Dynamic density forecasting using machine learning (with L.Hanus)

Tales of sentiment driven tails (with W.Hardle and C.Chen) slides available

Horizon-specific risks, higher moments, and asset prices (with J.Kurka)


Current (Full-Time) 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

Past Doctoral Students

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


JEM005 - 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.

web page of the course

JEM059 - Quantitative Finance I (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.

web page of the course

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.

web page of the course

JED412,413 - Nonlinear Dynamic Economic Systems: Theory and Applications (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.

web page of the course


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

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