Ostap Okhrin

Curriculum Vitae

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Professor

Name

Mr Dr. rer. pol. Ostap Okhrin

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Organization Name

Professur für Angewandte Statistik

Professur für Angewandte Statistik

Address work

Visiting address:

Falkenbrunnen, 1. Obergeschoß, Raum 125 Würzburger Str. 35

01187 Dresden

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work Tel.

fax Fax

+49 351 463-36809

Office hours:

nach Vereinbarung

Gepris, Google Scholar, RL Dresden

Professional Positions

Books

• Gòrecki, J., and Okhrin, O. Hierarchical Archimedean Copulas, Springer, 2024, Softcover ISBN: 978-3-031-56336-2, eBook ISBN: 978-3-031-56337-9
• Steland, A, Rafajlowicz, E., and Okhrin, O. Stochastic Models, Statistics and Their Applications, Springer, 2017, Hardcover ISBN: 978-3-319-55335-1, eBook ISBN: 978-3-030-28664-4
• Härdle, W.K., Okhrin, O., and Okhrin, Y. Basic Elements of Computational Statistics, Springer International Publishing, 2017, Hardcover ISBN:978-3-319-55335-1, eBook ISBN: 978-3-319-55336-8

Peer-reviewed publications

• Okhrin, O., Rockinger, M. and Schmid, M., Observations Concerning the Estimation of Hestons’ Stochastic Volatility Model Using HF Data, to appear in Statistical Papers

• Gillmann, N. and Okhrin, O., Adaptive local VAR for dynamic economic policy uncertainty spillover, Economic Modelling, 148, 107079, 2025, DOI: 10.1016/j.econmod.2025.107079

• Waltz, M., N. Paulig, N., and Okhrin, O., 2-level reinforcement learning for ships on inland waterways: Path planning and following, Expert Systems with Applications, 126933, 2025, DOI: 10.1016/j.eswa.2025.126933

• Kulkarni, M. M., Chaudhari, A. A., Srinivasan, K. K., Chilukuri, B. R., Treiber, M. and Okhrin, O., Leader-Follower Identification with Vehicle-Following Calibration for Non-Lane-Based Traffic, Transportation Research Part C: Emerging Technologies, 171, 104940, 2025, DOI: 10.1016/j.trc.2024.104940

• Li, D., and Okhrin, O., A platform-agnostic deep reinforcement learning framework for effective Sim2Real transfer towards autonomous driving, Communications Engineering, 3 (147), 2024, DOI: 10.1038/s44172-024-00292-3

• Paulig, N., and Okhrin, O., An open-source framework for data-driven trajectory extraction from AIS data - the alpha-method, Ocean Engineering, 312, 119092, 2024, DOI: 10.1016/j.oceaneng.2024.119092

• Waltz, M., and Okhrin, O., Addressing maximization bias in reinforcement learning with two-sample testing, Artificial Intelligence, 336, 104204, 2024, DOI: 10.1016/j.artint.2024.104204

• Hart, F., Waltz, M., and Okhrin, O., Two-step dynamic obstacle avoidance,  Knowledge-Based Systems, 302, 112402, DOI: 10.1016/j.knosys.2024.112402

• Zou, J., Odening, M., and Okhrin, O., Data-driven determination of plant growth stages for improved weather index insurance design, Agricultural Finance Review, 84(4/5), 297-319, 2024, DOI: 10.1108/AFR-01-2024-0015

•  Waltz, M., Okhrin, O., and Schultz, M., Self-organized free-flight arrival for urban air mobility, Transportation Research Part C: Emerging Technologies, 168, 104806, 2024, DOI: 10.1016/j.trc.2024.104806

• Jiang, H., Okhrin, O., and Rockinger, M., Artificial neural network small-sample-bias-corrections of the AR(1) parameter close to unit root, Statistica Neerlandica, 1-31, 2024, DOI: 10.1111/stan.12354

•  Paulig, N. and Okhrin, O., Robust path following on rivers using bootstrapped reinforcement learning, Ocean Engineering, 298, 117207, 2024, DOI: 10.1016/j.oceaneng.2024.117207

• Hart, F., Okhrin, O., and M. Treiber, Towards robust car-following based on deep reinforcement learning, Transportation Research Part C: Emerging Technologies, 104486, 2024, DOI: 10.1016/j.trc.2024.104486

• Chen, G., Fricke, H., Okhrin, O., and Rosenow, J. Flight delay propagation inference in air transport networks using the multilayer perceptron, Journal of Air Transport Management 114, 2024, 102510, DOI: 10.1016/j.jairtraman.2023.102510

• Genest, C., Okhrin, O., and Bodnar, T., Preface to the Special Issue entitled “Copula Modeling from Abe Sklar to the present day”, Journal of Multivariate Analysis 2024, 105280, DOI: 10.1016/j.jmva.2023.105280

• Hart, F., and Okhrin, O., Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk, Neurocomputing 568, 2024, 127097, DOI: 10.1016/j.neucom.2023.127097

• Okhrin, O., and Ristig, A., Penalized estimation of hierarchical Archimedean copula, Journal of Multivariate Analysis 2024, 105274, DOI: 10.1016/j.jmva.2023.105274

• Genest, C., Okhrin, O., and Bodnar, T., Copula modeling from Abe Sklar to the present day, Journal of Multivariate Analysis 2024, 105278, DOI: 10.1016/j.jmva.2023.105278

• Hautsch, N., Okhrin, O., and Ristig, A. Maximum-Likelihood Estimation Using the Zig-Zag Algorithm, Journal of Financial Econometrics, 2023, 1346-1375, DOI: 10.1093/jjfinec/nbac006

• Zou J., Odening M., and Okhrin O. Plant growth stages and weather index insurance design. Annals of Actuarial Science 17(3), 2023, pp. 438-458. DOI: 10.1017/S1748499523000167

• Hart, F., Okhrin, O., and Treiber, M., Vessel-following model for inland waterways based on deep reinforcement learning, Ocean Engineering 281, 2023, 114679, DOI: 10.1016/j.oceaneng.2023.114679

• Waltz, M., and Okhrin, O., Spatial–temporal recurrent reinforcement learning for autonomous ships, Neural Networks 165, 2023, pp. 634-653, DOI: 10.1016/j.neunet.2023.06.015

• Liebscher, E., and Okhrin, O., Semiparametric estimation of the high-dimensional elliptical distribution, Journal of Multivariate Analysis 195, 2023, 105142, DOI: 10.1016/j.jmva.2022.105142

• Li, D., and Okhrin, O. Modified DDPG car-following model with a real-world human driving experience with CARLA simulator, Transportation Research Part C: Emerging Technologies, 147, 2023, 103987, DOI: 10.1016/j.trc.2022.103987

• Okhrin, O., Rockinger, M., and Schmid, M. Distributional properties of continuous time processes: from CIR to Bates, AStA Advances in Statistical Analysis, 107, pages 397–419, 2023, DOI: 10.1007/s10182-022-00459-3

• Brunow, S., Lösch, S., and Okhrin, O. Labor Market Tightness and Individual Wage Growth: Evidence from Germany, Journal for Labour Market Research 56, (16), 2022. https://doi.org/10.1186/s12651-022-00322-7

• Okhrin, O., Rockinger, M., and Schmid, M. Simulating the CIR and Heston Processes: Matching the First Four Moments, Journal of Computational Finance, 2022, pp. 1-52, Vol. 26, No. 2, DOI: 10.21314/JCF.2022.022

• Waltz, M., Singh, A. K., and Okhrin, O. Vulnerability-CoVaR: investigating the cryptomarket, Quantitative Finance 2022, DOI: 10.1080/14697688.2022.2063166

• Chen, G, Fricke, H., Okhrin, O., and Rosenow, J. Importance of Weather Conditions in a Flight Corridor, Stats 5, 2022, pp. 312–338, (with ), DOI: 10.3390/stats5010018

• Chaudhari, A., Srinivasan, K., Chilukuri, B., Treiber, M., and Okhrin, O. Optimization of Wiedemann-99 Model Parameters for Mixed Traffic Using Vehicular Trajectory Data, to appear in Transportation Research Record, DOI:10.1177/03611981211037543

• Okhrin, O., Trimborn, S., and Waltz, M. gofCopula: Goodness-of-Fit tests for copulae, R Journal 13:1, pages 467-498, 2021, DOI: 10.32614/RJ-2021-060

• Maciak M., Okhrin, O., and Pesta, M. Infinitely Stochastic Micro Reserving, Insurance: Mathematics and Economics 100, 2021, DOI:10.1016/j.insmatheco.2021.04.007

• Größer, J. and Okhrin, O. Copulae: An Overview and Recent Developments, WIREs Computational Statistics e1557, 2021 DOI:10.1002/wics.1557

• Gorecki, J., Hofert, M., and Okhrin, O., Outer power transformations of hierarchical Archimedean copulas: Construction, sampling and estimation, Computational Statistics and Data Analysis, 155, 2021, DOI: 10.1016/j.csda.2020.107109

• Okhrin, O., Fischer, N., Statistical modeling of the required space for inland vessels, Communications in Statistics: Case Studies, Data Analysis and Applications,

  6(2), 2020, pp. 167-190, DOI: 10.1080/23737484.2020.1746934

• Bodnar, T., Okhrin, O. and Parolya, N., Optimal Shrinkage Estimator for High-Dimensional Mean Vector, Journal of Multivariate Analysis, 170, 2019, pp. 63-79, DOI: 10.1016/j.jmva.2018.07.004
• Audrino, F., Huang, C. and Okhrin, O., Flexible HAR Model for Realized Volatility,  Studies in Nonlinear Dynamics & Econometrics, 23(3), 2019, DOI: 10.1515/snde-2017-0080
• Khakimova, D., Loesch, S., Wende, D., Wiesmeth, H., Okhrin, O., Index of Environmental Awareness through MIMIC Approach, Papers in Regional Science, 98, 2019, pp. 1419-1441, DOI: 10.1111/pirs.12420
• Okhrin, O., Loesch, S. and Wiesmeth, H., Awareness of Climate Change: Difference Among Russian Regions, Area Development and Policy, 4(3), 2019, 284-307, DOI: 10.1080/23792949.2018.1514982
• Shen, Z., Odening, M. and Okhrin, O., Adaptive local parametric estimation of crop yields: Implications for crop insurance ratemaking, European Review of Agricultural Economics 45(2), (2018), pp. 173-203, DOI: 10.1093/erae/jbx028
• Genest, C., Plante, J.-F., and Okhrin, O., Qui se ressemblent s'assemblent, Accromath, 13(1), (2018), pp. 14-19
• Okhrin, O. and Xu, Y.-F., A Comparison Study of Pricing Credit Default Swap Index Tranches with Convex Combination of Copulae, The North American Journal of Economics and Finance 42, (2017), pp. 193-217, DOI: 10.1016/j.najef.2017.07.004 (slides)
• Okhrin, O. and Tetereva, A., The realized hierarchical Archimedean copula in risk modelling, Econometrics 5(2), 26, 2017, DOI: 10.3390/econometrics5020026
• Härdle, W., López Cabrera, B., Okhrin, O. and Wang, W., Localising temperature risk, Journal of the American Statistical Association, 111(516), 2016, pp. 1491-1508 , DOI: 10.1080/01621459.2016.1180985 (working paper version, slides)
• Zhang, S., Okhrin, O., Zhou, Q., and Song, P., Goodness-of-fit Test For Specification of Semiparametric Copula Dependence Models, Journal of Econometrics, 193, 2016, pp. 215-233, DOI: 10.1016/j.jeconom.2016.02.017 (working paper version, slides, R package)
• Fengler, M. R. and Okhrin, O., Managing Risk with a Realized Copula Parameter, Computational Statistics and Data Analysis,100, 2016, pp. 131-152, DOI: 10.1016/j.csda.2014.07.011 (working paper version, slides)
• Choros-Tomczyk, B., Härdle, W. K., and Okhrin. O., A Semiparametric Factor Model for CDO Surfaces Dynamics, Journal of Multivariate Analysis, 146, 2016, pp. 151–163, DOI: 10.1016/j.jmva.2015.09.002 (working paper version, slides)
• Shen, Z., Odening, M., and Okhrin, O., Can expert knowledge compensate for data scarcity in crop insurance pricing?, European Review of Agricultural Economics 43(2), 2016, pp. 237-269, DOI: 10.1093/erae/jbv015
• Okhrin, O., Levy copulae for financial returns, Dependence Modeling, 4, 2016, pp. 288-305, DOI: 10.1515/demo-2016-0017
• Härdle, W.K., Okhrin, O., and Wang, W.,  HMM in dynamic HAC models, Econometric Theory 31(5), 2015, pp 981-1015, DOI: 10.1017/S0266466614000607 (working paper version, slides)
• Okhrin, O., and Trueck, S. Editorial to the special issue on Applicable semiparametrics of computational statistics, Computational Statistics 30(3), 2015, pp 641-646, DOI: 10.1017/s00180-015-0616-4
• Cao, X., Okhrin, O., Odening, M., and Ritter, M., Modelling spatiotemporal variability of temperature, Computational Statistics 30(3), 2015, pp 745-766, DOI: 10.1007/s00180-015-0561-2 (working paper version)
• Durante, F. and Okhrin, O., Estimation procedures for exchangeable Marshall copulas with application to hydrological risk,  Stochastic Environmental Research and Risk Assessment 29, 2014, pp 205-226, DOI: 10.1007/s00477-014-0866-7 (working paper version)
• Okhrin, O. and Ristig, A., Hierarchical Archimedean Copulae: The HAC Package, Journal of Statistical Software 58(4), 2014, pp 1-20 (working paper version, R package)
• Pesta, M. and Okhrin, O., Conditional Least Squares and Copulae in Claims Reserving for a Single Line of Business, Insurance: Mathematics and Economics 56, 2014, pp 28-37, DOI: 10.1016/j.insmatheco.2014.02.007 (working paper version)
• Zolotko, M. and Okhrin, O., Modelling general dependence between commodity forward curves, Energy Economics 43, 2014, pp 284-296, DOI: 10.1016/j.eneco.2014.02.019 (working paper version)
• Okhrin, O., Okhrin, Y. and Schmid, W., Determining the structure and estimation of hierarchical Archimedean copulas, Journal of Econometrics 173(2), 2013, pp. 189-204, DOI: 10.1016/j.jeconom.2012.12.001 (slides)
• Okhrin, O., Editorial to the special issue on Copulae of Statistics & Risk Modeling, Statistics and Risk Modeling (former Statistics and Decisions), 30(4), 2013, pp.281-286, DOI:10.1524/strm.2013.9014
• Härdle, W. K., Okhrin, O. and Okhrin, Y., Dynamic Structured Copula Models, Statistics and Risk Modeling (former Statistics and Decisions), 30(4), 2013, pp.361-388, DOI: 10.1524/strm.2013.2004 (working paper version, slides)
• Choros, B., Härdle, W. and Okhrin, O., Valuation of Collateralized Debt Obligations with Hierarchical Archimedean Copulae, Journal of Empirical Finance 24, 2013, pp. 42-62, DOI: 10.1016/j.jempfin.2013.08.001 (working paper version, slides)
• Okhrin, O., Okhrin, Y. and Schmid, W., Properties of hierarchical Archimedean copulas. Statistics and Risk Modeling (former Statistics and Decision) 30(1), 2013, pp. 21-53, DOI: 10.1524/strm.2012.1107 (working paper version, slides)
• Okhrin, O., Odening, M. and W. Xu, W., Systemic Weather Risk and Crop Insurance: The Case of China, Journal of Risk and Insurance 80(2), 2013, pp 351-372, DOI: 10.1111/j.1539-6975.2012.01476.x (working paper version, slides)
• Okhrin, O., On the Generating Functional of the special case of S-Stopped Branching Processes, Visn. L'viv. Univ., Ser. Mekh.-Mat. (Bulletin of the Lviv University), Series in Mechanics and Mathematics 74, 2011, pp. 157-167 (working paper version, slides)
• Odening, M., Filler, G., Okhrin, O. and Xu, W., On the Systemic Nature of Weather Risk. Agricultural Finance Review 70(2), 2010, pp. 267-284. (working paper version, slides)
• Härdle, W. and Okhrin, O., De copulis non est disputandum - Copulae: an Overview, AStA - Advances in Statistical Analysis 94(1), 2010, pp. 1-31 (working paper version)
• Yeleyko, Y., Kyrychynska, I. and Okhrin, O., Asymptotic behavior of the S-stopped branching processes with countable state space, Visn. L'viv. Univ., Ser. Mekh.-Mat. (Bulletin of the Lviv University), Series in Mechanics and Mathematics 67, 2007, pp. 119-129 (working paper version, slides)

Other Publications

• Okhrin, O., A Nonparametric Multivariate Statistical Process Control Chart Based on Change Point Model, in: Zh. Zhang, K.-H. Yuan, Y. Wen and J. Tang (eds.), New Developments in Data Science and Data Analytics, Proc. of the 2019 Meeting of International Society for Data Science and Analytics, 2019
• Loesch, S., Okhrin, O. and Wiesmeth, H., Awareness of climate change (focus on the Russian Arctic zone), Proceedings of the International Research Workshop on Information Technologies and Mathematical Modeling for Efficient Development of Arctic Zone, Yekaterinburg, Russia, April 19-21, 2018, pp. 38-42
• Fischer, N., Treiber, M. and Okhrin, O., Fahrdynamikbasierte Entscheidungsmodelle zur mikroskopischen Simulation des Verkehrsflusses auf Binnenwasserstraßen, Bundesanstalt für Wasserbau (BAW) (publisher): Wasserbauliche Herausforderungen an den Binnenschifffahrtsstraßen. Karlsruhe: Bundesanstalt für Wasserbau (BAW), pp. 61-66, 2017 (slides)
• Lösch, S., Okhrin, O. and Wiesmeth, H., Diffusion of Environmental Awareness, Diffusion Fundamentals 30, 2017, pp. 1-16
• Okhrin, O., Ristig, A. and Xu, Y., Copulae in High Dimensions: An Introduction, in: C. Chen, W. K. Hardle and L. Overbeck (eds.), Applied Quantitative Finance, third ed. (2017), Springer Verlag, , pp. 247-277, DOI: 10.1007/978-3-662-54486-0 13
• Odening, M., Okhrin, O., Shen, Z., Can expert knowledge compensate for data scarcity in crop insurance pricing?, Selected paper AAEA Annual Meeting 2013, Washington D.C. 
• Hautsch, N., Okhrin, O., Ristig, A., Modeling Time-Varying Dependencies between Positive-Valued High-Frequency Time Series, in: P.Jaworski, F.Durante, and W.K.Härdle (eds.), Copulae in Mathematical and Quantitative Finance, (2013), Springer Verlag
• Härdle, W., Wang, W., HMM and HAC, Advances in Intelligent Systems and Computing Volume 190, 2013, pp. 341-348, DOI: 10.1007/978-3-642-33042-1_37
• Härdle, W., Okhrin, O. and Choros, B., CDO Pricing with Copulae. In Bulletin of the International Statistical Institute, 57th Session Durban Vol. 57. Bulletin of the International Statistical Institute, 2009 (working paper version)
• Okhrin, O., Fitting high-dimensional Copulae to Data, in: J.-C. Duan, J. E. Gentle, and W. K. Härdle (eds.), Handbook of Computational Finance, (2011) Springer Verlag, pp. 469-503. (working paper version, slides)
• Härdle, W., Okhrin, O., and Okhrin, Y., Modeling Dependencies in Finance using Copulae, Applied Quantitative Finance, eds. W. Härdle, N. Hautsch and L. Overbeck, second edition, 2008, Springer Verlag, pp. 3-36 (working paper version, slides)
• Okhrin, O., Yatsyshynets (Okhrin), I., and Yeleyko, Ya., Portfolio selection based on the internal yield requirement, proceedings of 7th international workshop for young mathematicians: Applied Mathematics, Cracow, 2005, pp. 131-149
• Renewal theory and stock returns, Applied statistics. Acturial and Financial Mathematics. #1-2, pp. 217-218, 2004 (in Ukrainian)