Development of the insurance market in Ukraine and forecasting its crises
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Author(s)Viktoriia Babenko-LevadaLink to ORCID Index: https://orcid.org/0000-0002-4249-5369
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DOIhttp://dx.doi.org/10.21511/imfi.18(3).2021.32
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Article InfoVolume 18 2021, Issue #3, pp. 385-396
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Insurance market is an important part of the financial market, the functioning of which helps to protect individuals and legal entities from the negative and stressful effects of today’s unstable economic environment. The purpose of this study is to determine trends in the insurance market in Ukraine and its potential crises.
The study found that Ukraine’s insurance market constantly grows, but is volatile and in a state of concentration. The dynamics of most indicators are cyclical, with a cycle length from 4,66 quarters to 14 quarters.
The randomized R/S-analysis confirmed the stability of the dynamics of Ukraine’s insurance market and its fractal similarity. Fractal similarity was proved for six out of ten analyzed indicators of the insurance market. In addition, it was confirmed that at the moment of transition from one fractal to another, a trend break occurs. Thus, the emergence of crises on the insurance market of Ukraine is associated with the self-similarity of the dynamics and the coincidence of the moments of bifurcation of certain indicators in its development. A partial crisis on the Ukrainian insurance market at the beginning of 2019 coincided with the bifurcation of the number of concluded insurance contracts, determined based on the results of fractal analysis.
Calculations made it possible to conclude that potentially crisis periods for the insurance market of Ukraine fall on Q1-2 2017, Q1 2019, Q1 2020, of which only one was realized (Q1 2019). The nearest potential moments of crises on the insurance market of Ukraine may be the following periods: Q1 2023 and Q1 2026.
- Keywords
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JEL Classification (Paper profile tab)C10, C53, G22
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References32
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Tables2
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Figures2
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- Figure 1. Stages of determining potential moments of bifurcation in the insurance market
- Figure. 2. Dynamics of the volume of insurance payments in the Ukrainian insurance market for first-order fractals 2014–2020 in the period
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- Table 1. Description of lines of theoretical approximation dynamics of indicators for Ukraine’s insurance market
- Table 2. Theoretical approximation of lines of the insurance market dynamics by first-order fractals
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- Amadeo, K. (2019). Insurance Industry Trends and Outlook. The balance.
- Anderson, N., & Noss, J. (2013). The Fractal Market Hypothesis and its implications for the stability of financial markets. Vox EU CEPR.
- Baluch, S., Mutenga, S., & Parsons, C. (2011). Insurance, Systemic Risk and the Financial Crisis. The Geneva Papers, 36, 126-163.
- Dalton, L. (2006). Time Series Analysis Using Fractal Theory and Online Ensemble Classifiers with Application to Stock Portfolio Optimization (70 р.). Johannesburg: University of the Witwatersrand.
- Ehanova, E. A., & Kallys, V. (2017). Temporary structure of complex systems. Mathematical Structures and Modeling, 1(41), 12-25.
- Forinsurer. (n.d.). Statistika strakhovogo rynka Ukrainy [Statistics of the insurance market of Ukraine]. (In Russian).
- Fouladvand, M. E., & Darooneh, A. (2004). Premium Forecasting of an Insurance Company. The Application of Econophysics, 303-308.
- Gachkov, A. A. (2009). Randomized algorithm for R/S analysis of financial series. Stochastic Optimization in Computer Science, 5, 40-64.
- Gestel, T. V., Martens, D., Baesens, B., Feremans, D., Huysmans, J., & Vanthienen, J. (2007). Forecasting and analyzing insurance companies’ ratings. International Journal of Forecasting, 23(3), 513-529.
- Izzeldin, M. A. (2007). Trading volume and the number of trades: a comparative study using high frequency data (19 p.). Lancaster: Lancaster University Management School.
- Kapecka, A. (2013). Fractal Analysis of Financial Time Series Using Fractal Dimension and Pointwise Hölder Exponents. Dynamic Econometric Models, 13, 107-125.
- Kozmenko, O. V., Merenkova, О. V., & Boyko, A. O. (2009). The analysis of insurance market structure and dynamics in Ukraine, Russia and European insurance and Reinsurance Federation (СЕА) member states. Problems and Perspectives in Management, 7(1), 29-39.
- Kozmenko, O. V., Merenkova, О. V., Boyko, A. O., & Kravchuk, H. (2009). Forecasting of principal directions of Ukrainian insurance market development based on German insurance market indices. Innovative Marketing, 5(4), 51-54.
- Lenten L. J. A., & Rulli D. N. (2006). A Time-Series Analysis of the Demand for Life Insurance Companies in Australia: An Unobserved Components Approach. Australian Journal of Management, 31, 41-56.
- Lytvin, A. V. (2013). Pobudova modelei prohnozuvannia bankrutstva strakhovykh kompanii Ukrainy v pisliakryzovyi period [Developing bankruptcy prediction models for Ukrainian insurance companies in post-crisis period]. Ekonomichnyi Analiz – Economic Analysis, 1, 282-300. (In Ukrainian).
- Mandelbrot, B. B. (1977). Fractals: Form, Chance, and Dimension (352 p.). San Francisco: W. H. Freeman and Company.
- MAPFRE Economics Review. (n.d.). Industry Outlook for the Insurance Market (2Q-2020). Madrid: Fundación.
- Marsh. (n.d.). Global Insurance Market Index – 2021 Q1.
- Melnyk, M., Zhabynets, O., Myshchyshyn, I., & Orlov, V. (2021). Efficient use of the insurance sector potential adjusted for its shadowing: Case of Ukraine. Insurance Markets and Companies, 12(1), 16-31.
- Nazarova, O. (2018). Analiz funktsionuvannia rynku strakhovykh posluh [Analysis of the functioning of the insurance services market]. Ekonomika i suspilstvo – Economy and society, 14, 984-990. (In Ukrainian).
- OECD. (2020). Global insurance market trends.
- Ogilvi, T. (2021). 2020 Insurance Trends: Good Vibrations for Agile Insurers. Peer Insight.
- Polinkevych, O., & Kamiński, R. (2020). Anti-crisis development strategies of insurance companies in Ukraine and Poland in the context of COVID-19. Insurance Markets and Companies, 11(1), 21-29.
- Pozdniakova, L. O., & Mamonova, H. V. (2011). The use of economic and mathematical methods of modeling in insurance processes. Actual Problems of Economics, 278-284.
- Proskurovych, O. V., & Melnychuk, I. I. (2014). Econometric modeling of performance of insurance companies. Naukovyi Visnyk Khersonskoho Derzhavnoho Universytetu. Seriia “Ekonomichni Nauky” – Scientific Bulletin of Kherson State University. Economic Sciences Issue, 231-235.
- Richards, G. R. (2004). A Fractal forecasting model for financial time series. Journal of Forecasting, 23(8), 586-601.
- Schmitt, F., Schertzer, D., & Lovejoy, S. (2001). Multifractal fluctuations in finance. International Journal of Theoretical and Applied Finance, 3, 361-364.
- Serediuk, V. B. (2014) Prohnozuvannia finansovykh pokaznykiv strakhovoi kompanii iz zastosuvanniam instrumentariiy nechitkoi lohiky [Forecasting the financial performance of the insurance company using the fuzzy logic tools]. Visnyk Vinnytskoho Politekhnichnoho Instytutu – Bulletin of Vinnytsia Politechnic Institute, 2, 33-36. (In Ukrainian).
- Shkolnyk, I., Bondarenko, E., & Balev, V. (2017). Estimation of the capacity of the Ukrainian stock market’s risk insurance sector. Insurance Markets and Companies, 8(1), 34-47.
- State Statistics Service of Ukraine. (2021).
- Sviridov, O. Y., & Nekrasova, I. (2016). Fractal analysis of financial markets. International Research Journal, 10(52), 74-79.
- Timashova, L., & Skachko, I. (2016). Financial market forecasting using the fractal analysis. Lviv Polytechnic National University Institutional Repository, 116-121.
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Interest in universities based on search queries on the Internet
Yulia Bondarenko ,
Solomiya Ohinok ,
Artur Kisiołek ,
Oleh Karyy 
doi: http://dx.doi.org/10.21511/im.17(3).2021.15
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Regulating Big Data effects in the European insurance market
Donatella Porrini
doi: http://dx.doi.org/10.21511/ins.08(1).2017.01
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Financial market imbalance: reasons and peculiarities of occurrence in Ukraine
Rostyslav Slav’yuk ,
Lyudmyla Shkvarchuk ,
Iryna Kondrat
doi: http://dx.doi.org/10.21511/imfi.14(1-1).2017.09
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