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 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 (Recent) Publications

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

Asymmetric Network Connectedness of Fears (with M.Bevilacqua, and R.Tunaru)
R&R in The Review of Economics and Statistics (2019)

Abstract: We study how shocks to the forward-looking expectations of future stock prices, extracted from call and put options, create asymmetric network connections. We introduce a new measure of network connectedness, called asymmetric fear connectedness, which captures the information related to fear on both sides of the options market, and that can be a useful forward-looking systemic risk monitoring tool. The decomposed connectedness measures provide timely predictive information for near-future macroeconomic and uncertainty indicators, and they contain additional valuable information not included in the aggregate network connectedness measure.

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

Quantile Spectral Beta: A Tale of Tail Risks, Investment Horizons, and Asset Prices (with M.Nevrla)
Submitted (2019)

Abstract: We examine how extreme market risks are priced in the cross-section of asset returns at various horizons. Based on the decomposition of covariance between indicator functions capturing fluctuations of different parts of return distributions over various frequencies, we define a quantile spectral beta representation that characterizes asset’s risk generally. Nesting the traditional frameworks, the new representation explains tail-specific as well as horizon-, or frequency-specific spectral risks. Further, we work with two notions of frequency-specific extreme market risks. First, we define tail market risk that captures dependence between extremely low market and asset returns. Second, extreme market volatility risk is characterized by dependence between extremely high increments of market volatility and extremely low asset return. Empirical findings based on the datasets with long enough history, 30 Fama-French Industry portfolios, and 25 Fama-French portfolios sorted on size and book-to-market support our intuition. We reach the same conclusion using stock-level data as well as daily data. These results suggest that both frequency-specific tail market risk and extreme volatility risk are priced and our final model provides significant improvement over specifications considered by previous literature.

Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns (with F.Cech)
R&R in The Journal of Financial Markets (2019)

Abstract: This paper investigates how to measure common market risk factors using newly proposed Panel Quantile Regression Model for Returns. By exploring the fact that volatility crosses all quantiles of the return distribution and using penalized fixed effects estimator we are able to control for otherwise unobserved heterogeneity among financial assets. Direct benefits of the proposed approach are revealed in the portfolio Value-at-Risk forecasting application, where our modeling strategy performs significantly better than several benchmark models according to both statistical and economic comparison. In particular Panel Quantile Regression Model for Returns consistently outperforms all the competitors in the 5% and 10% quantiles. Sound statistical performance translates directly into economic gains which is demonstrated in the Global Minimum Value-at-Risk Portfolio and Markowitz-like comparison. Overall results of our research are important for correct identification of the sources of systemic risk, and are particularly attractive for high dimensional applications.

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

Work in Progress

Asset Pricing using Time-Frequency Dependent Network Centrality (with M. Ellington)

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

Asset Pricing with Quantile Machine Learning (with A.Galvao and M.Hronec)

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

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

Dynamic quantile model for bond pricing (F.Cech)


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