doc. PhDr. Jozef Baruník, Ph.D.

Fundamental and applied research in financial econometrics, statistical methods for economists, and econometrics. The main interest of research is in fields of asset pricing, high- frequency data, dynamic networks, machine learning, high-dimensional data sets.

Education

2011 PhD in Economics, Charles University in Prague
2006 PhDr. in Economics, Charles University in Prague
2006 Mgr.(MSc. equivalent), in Economics, Charles University in Prague
2004 Bc. (BSc. equivalent) in Economics, Charles University in Prague

Job history

2021 - 2023 external consultant, Market Intelligence division, Bank of England
2017 (April)+: Associate Professor, Charles University in Prague
2011 (Oct.) - 2017 (March): Assistant Professor, Charles University in Prague
2013 + Research Fellow (Deputy Head of the Department from 2014), Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Department of Econometrics
2011 - 2012 PostDoc, Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Department of Econometrics

Monographs

Chapters in monographs

• Baruník J., Kočenda E., & Vácha L. (2014). Wavelet-Based Correlation Analysis of the Key Traded Assets. Wavelet Applications in Economics and Finance (pp. 157-183).

Articles

• Baruník J., Bevilacqua M., & Tunaru R. (2022). Asymmetric Network Connectedness of Fears. Review of Economics and Statistics, 104(6), 1304-1316. UT-WOS link
• Baruník J., & Kley T. (2019). Quantile coherency: A general measure for dependence between cyclical economic variables. Econometrics Journal, 22(2), 131-152. UT-WOS link
• Baruník J., & Čech F. (2021). Measurement of common risks in tails: A panel quantile regression model for financial returns. Journal of Financial Markets, 52(January), 1-24. UT-WOS link
• Baruník J., & Malinská B. (2016). Forecasting the term structure of crude oil futures prices with neural networks. Applied Energy, 164(April), 366-379. UT-WOS link
• Baruník J., & Hlínková M. (2016). Revisiting the long memory dynamics of the implied-realized volatility relationship: New evidence from the wavelet regression. Economic Modelling, 54(April), 503-514. UT-WOS link
• Baruník J., Kočenda E., & Vácha L. (2016). Gold, oil, and stocks: Dynamic correlations. International Review of Economics and Finance, 42(March 01), 186-201. UT-WOS link
• Baruník J., Kočenda E., & Vácha L. (2016). Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers. Journal of Financial Markets, 27(January), 55-78. UT-WOS link
• Baruník J., Kočenda E., & Vácha L. (2015). Volatility Spillovers Across Petroleum Markets. Energy Journal, 36(3), 309-329. UT-WOS link
• Baruník J., & Kukačka J. (2015). Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility. Quantitative Finance, 15(6), 959-973. UT-WOS link
• Baruník J., & Vácha L. (2015). Realized wavelet-based estimation of integrated variance and jumps in the presence of noise. Quantitative Finance, 15(8), 1347-1364. UT-WOS link
• Baruník J., & Dvořáková S. (2015). An Empirical Model Of Fractionally Cointegrated Daily High And Low Stock Market Prices. Economic Modelling, 45(February), 193-206. UT-WOS link
• Baruník J., & Křehlík T. (2016). Combining high frequency data with non-linear models for forecasting energy market volatility. Expert Systems with Applications, 55(15 August 2016), 222-242. UT-WOS link
• Baruník J. (2008). How do neural networks enhance the predictability of Central European stock returns?. Finance a úvěr = Czech Journal of Economics and Finance, 58(7-8), 359-376.
• Baruník J., & Soták B. (2010). Vplyv rôznych foriem vlastníctva na efektivitu českých a slovenských bánk : prístup analýzy stochastických hraníc. Politická ekonomie, 58(2), 207-224. UT-WOS link
• Baruník J., & Vácha L. (2010). Monte Carlo-based tail exponent estimator. Physica A: Statistical Mechanics and its Applications, 389(21), 4863-4874. UT-WOS link
• Baruník J., & Krištoufek L. (2010). On Hurst exponent estimation under heavy-tailed distributions. Physica A: Statistical Mechanics and its Applications, 389(18), 3844-3855. UT-WOS link
• Baruník J., & Křehlík T. (2018). Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk. Journal of Financial Econometrics, 16(2), 271-296. UT-WOS link
• Baruník J., Vácha L., & Vošvrda M. (2010). Tail behavior of the Central European stock markets during the financial crisis. Acta Universitatis Carolinae. Oeconomica, Czech Economic Review, 4(3), 281-294.
• Baruník J. (2010). L. E. Calvet & A. J. Fisher, Multifractal volatility : theory, forecasting, and pricing. Acta Universitatis Carolinae. Oeconomica, Czech Economic Review, 4(3), 341-343.
• Baruník J., & Vošvrda M. (2009). Can a stochastic cusp catastrophe model explain stock market crashes?. Journal of Economic Dynamics and Control, 33(10), 1824-1836. UT-WOS link
• Baruník J., Vácha L., & Vošvrda M. (2009). Smart predictors in the heterogeneous agent model. Journal of Economic Interaction and Coordination, 4(2), 163-172. UT-WOS link
• Baruník J., & Vácha L. (2018). Do co-jumps impact correlations in currency markets?. Journal of Financial Markets, 37(January), 97-119. UT-WOS link
• Baruník J., Křehlík T., & Vácha L. (2016). Modeling and forecasting exchange rate volatility in time-frequency domain. European Journal of Operational Research, 251(1), 329-340. UT-WOS link
• Baruník J., Kočenda E., & Vácha L. (2017). Asymmetric volatility connectedness on the forex market. Journal of International Money and Finance, 77(October), 39-56. UT-WOS link
• Baruník J., Aste T., Di Matteo T., & Liu R. (2012). Understanding the source of multifractality in financial markets. Physica A: Statistical Mechanics and its Applications, 391(17), 4234-4251. UT-WOS link
• Baruník J., & Vácha L. (2013). Contagion among Central and Eastern European Stock Markets during the Financial Crisis. Finance a úvěr, 63(5), 443-453. UT-WOS link
• Baruník J., & Kočenda E. (2019). Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets. Energy Journal, 40(SI2), 157-174. UT-WOS link
• Baruník J., & Nevrla M. (2023). Quantile Spectral Beta: A Tale of Tail Risks, Investment Horizons, and Asset Prices*. Journal of Financial Econometrics, 21(5), 1590-1646. UT-WOS link
• Apergis N., Baruník J., & Lau M. (2017). Good volatility, bad volatility: What drives the asymmetric connectedness of Australian electricity markets?. Energy Economics, 66(August), 108-115. UT-WOS link
• Křehlík T., & Baruník J. (2017). Cyclical properties of supply-side and demand-side shocks in oil-based commodity markets. Energy Economics, 65(June), 208-218. UT-WOS link
• Avdulaj K., & Baruník J. (2013). Can We Still Benefit from International Diversification? The Case of the Czech and German Stock Markets. Finance a úvěr, 63(5), 425-442. UT-WOS link
• Kraicová L., & Baruník J. (2017). Estimation of long memory in volatility using wavelets. Studies in Nonlinear Dynamics and Econometrics, 21(3), nestránkováno. UT-WOS link
• Kukačka J., & Baruník J. (2013). Behavioural breaks in the heterogeneous agent model: the impact of herding, overconfidence, and market sentiment. Physica A: Statistical Mechanics and its Applications, 392(23), 5920-5938. UT-WOS link
• Čech F., & Baruník J. (2017). On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model. Journal of Forecasting, 36(2), 181-206. UT-WOS link
• Čech F., & Baruník J. (2019). Panel quantile regressions for estimating and predicting the value-at-risk of commodities. Journal of Futures Markets, 39(9), 1167-1189. UT-WOS link
• Kukačka J., & Baruník J. (2017). Estimation of financial agent-based models with simulated maximum likelihood. Journal of Economic Dynamics and Control, 85(December), 21-45. UT-WOS link
• Anatolyev S., & Baruník J. (2019). Forecasting dynamic return distributions based on ordered binary choice. International Journal of Forecasting, 35(3), 823-835. UT-WOS link
• Avdulaj K., & Baruník J. (2017). A semiparametric nonlinear quantile regression model for financial returns. Studies in Nonlinear Dynamics and Econometrics, 21(1), 81-97. UT-WOS link
• Vácha L., Baruník J., & Vošvrda M. (2010). Smart agents and sentiment in the heterogeneous agent model. ERCIM News, Neuveden(81), 39-40.
• Vošvrda M., & Baruník J. (2008). Modelování krachů na kapitálových trzích : aplikace teorie stochastických katastrof. Politická ekonomie, 56(6), 759-771. UT-WOS link
• Vácha L., Baruník J., & Vošvrda M. (2009). Smart agents and sentiment in the heterogeneous agent model. Prague Economic Papers, 18(3), 209-219. UT-WOS link
• Franta M., Baruník J., Horváth R., & Šmídková K. (2014). Are Bayesian Fan Charts Useful? The Effect of Zero Lower Bound and Evaluation of Financial Stability Stress Tests. International Journal of Central Banking, 10(1), 159-187. UT-WOS link
• Žikeš F., Baruník J., & Shenai N. (2017). Modeling and Forecasting Persistent Financial Durations. Econometric Reviews, 36(10), 1081-1110. UT-WOS link
• Žikeš F., & Baruník J. (2016). Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility. Journal of Financial Econometrics, 14(1), 185-226. UT-WOS link
• Avdulaj K., & Baruník J. (2015). Are benefits from oil-stocks diversification gone? New evidence from a dynamic copula and high frequency data. Energy Economics, 2015(51), 31-44. UT-WOS link
• Baruníková M., & Baruník J. (2011). Neural Networks as Semiparametric Option Pricing Tool. Bulletin of the Czech Econometric Society, 18(28), 66-83.
• Vácha L., Baruník J., & Vošvrda M. (2012). How do skilled traders change the structure of the market. International Review of Financial Analysis, 23(June), 66-71. UT-WOS link
• Vácha L., & Baruník J. (2012). Co-movement of energy commodities revisited: evidence from wavelet coherence analysis. Energy Economics, 34(1), 241-247. UT-WOS link

Contributions in the conference proceedings

• Baruník J., & Vácha L. (2011). Modeling multivariate volatility using wavelet-based realized covariance estimator. Mathematical methods in economics 2011 : 29th International conference : proceedings : September 6-9 2011, Janská Dolina, Slovakia (pp. 29-34). UT-WOS link
• Baruník J., Kočenda E., & Vácha L. (2014). Asymmetric connectedness of markets: How does the good and bad volatility spills over the US industries?. INTERNATIONAL WORK-CONFERENCE ON TIME SERIES (ITISE 2014) (pp. 185-185). UT-WOS link
• Baruník J., Vácha L., & Krištoufek L. (2010). Comovement of Central European stock markets using wavelet coherence: Evidence from high-frequency data. Mathematical Methods in Economics (pp. 12-17).
• Baruník J., & Vošvrda M. (2008). Application of CUSP catastrophe theory to U.S. stock market crashes. Quantitative methods in economics : multiple criteria decision making XIV (pp. 19-27).
• Baruník J., & Vošvrda M. (2008). Cusp catastrophe theory : application to U.S. stock. Mathematical Methods in Economics 2008 : proceedings of 26th international conference, September 17-19, 2008 (pp. 1-1).
• Baruník J., & Vácha L. (2008). Neural networks with Wavelet based denoising layer : application to Central European stock market forecasting. Mathematical Methods in Economics 2008 : proceedings of 26th international conference, September 17-19, 2008 (pp. 1-1).
• Vácha L., & Baruník J. (2009). What does the wavelet analysis tell us about the Central European stock markets behavior during the crisis?. Mathematical methods in economics 2009 (pp. 163-172).
• Vácha L., & Baruník J. (2008). Wavelet neural networks prediction of Central European stock markets. Quantitative methods in economics : multiple criteria decision making XIV (pp. 291-297).

My lectures and seminars consecutively rank among “Best courses taught at the IES” in years 2010 – 2023.

2015 “Energy Economics Contest Award” 1st place (joint work with B.Malinska)
2014 “Economic Research Award” (Czech National Bank)
2013 “Energy Economics Contest Award” 1st place (joint work with K.Avdulaj)
2012 “Otto Wichterle award by the Academy of Sciences of the Czech Republic” 2012 “ČEZ Corporate Chair holder (2012+) ”
2011 “Energy Economics Contest Award” 1st place (joint work with L.Vacha) 2011 “Czech Econometric Society Award” 1st place (Best Student Paper) 2010 “Czech Econometric Society Award” 3rd place (Best Student Paper)

JEM217 - Advanced Econometrics
JED412 - Advanced Financial Econometrics I
JED413 - Advanced Financial Econometrics II
JEM116 - Applied Econometrics
JEM059 - Financial Econometrics I
JEM061 - Financial Econometrics II

Bachelor theses

I welcome any topic in the field of Applied Financial Econometrics. The decision on the topic will be made after the discussion with the student.

Master theses

My research interest is in the development of mathematical models for understanding financial problems (such as measuring and managing financial risk), statistical methods and analyzis of financial data. Especially, topics in

Asset pricing
High-Frequency Data in Finance
Financial econometrics
Time Series Econometrics of Financial Markets
Machine learning, Artificial Intelligence, Neural Networks
Quantile Models
Nonparametric Data Analysis
High-dimensional financial data sets (big data)
Frequency domain econometrics (cyclical properties and behavior of economic variables).

2019-23 Czech Science Foundation (GA CR) EXPRO “Dynamic Models for the Digital Finance”, Principal Investigator
2016-18 Czech Science Foundation (GA CR) “New measures of dependence between economic variables”, Principal Investigator
2014-16 7th Framework Programme for EU research "Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents", Local Coordinator
2014-16 Czech Science Foundation (GA CR) "Dynamic correlations and financial market risk", Principal Investigator
2013-15 Czech Science Foundation (GA CR) "Multivariate spectral analysis of financial markets", Principal Investigator

Fundamental and applied research in financial econometrics, statistical methods for economists, and econometrics. The main interest of research is in fields of asset pricing, high- frequency data, dynamic networks, machine learning, high-dimensional data sets.