Увійти 
|
HOME PAGE | |
№ 2020/2
VASIURENKO Oleg Volodymyrovych1, LYASHENKO Viacheslav Viktorovych2
1Private Higher Education Institution "Kyiv Institute of Business and Technology"
2Kharkiv National University of Radio Electronics
Wavelet coherence as a tool for retrospective analysis of bank activities
Economy and forecasting 2020; 2:32-44 | https://doi.org/10.15407/econforecast2020.02.032 |
ABSTRACT ▼
The article considers the possibility and expediency of using the apparatus of the theory of wavelets to conduct analysis of banking activities. The authors determine separate stages of the complex application of various tools on the theory of wavelets to analyze the activities of banks based on retrospective data. Among these stages are: decomposition of the initial data by their approximating coefficients and coefficients of detail, and the use of wavelet coherence.
Indicated the importance of conducting a retrospective analysis to reveal hidden relationships in the data structure that determine certain aspects of banking. The ad-vantages of using the tools of the theory of wavelets from the point of view of analyzing the activities of banks based on their statistical data are highlighted. Among these advantages, the authors highlight the possibility of studying the relationships be-tween data over time and determining the depth of such relationships. It is noted that this can be done in one research window.
Particular attention is focused on the analysis of the reciprocity between the volume of funds in deposit accounts and the volume of loans granted, as one of the key parameters for conducting banking activities. The reciprocity between the volumes of funds in deposit accounts and the volumes of loans granted is revealed in accordance with the volumes of administrative expenses and equity of banks. It is noted that retrospective analysis allows us to identify the consequences of the onset of unwanted events and prevent them in the future.
To carry out a corresponding analysis, the content of constructing a description of spatial wavelet coherence is disclosed. Such a description makes it possible to take into account a larger number of parameters than classical approaches for calculating wavelet coherence. This expands the boundaries of the relevant analysis, allows you to explore various mutual influences between individual banks in terms of their individual indicators for banking activities. Such an analysis allows to determine not only the reciprocity between individual indicators of banking activity, but also the depth of influence between individual banks, taking into account such indicators of their activity. Concrete examples are given that prove the feasibility and likelihood of applying the proposed approaches to the analysis of banking activities.
Keywords:wavelet coherence, banking, time series, deposits, loans, administrative expenses, equity
Article in English (pp. 32 - 44) | Download | Downloads :382 |
REFERENCES ▼
2. Rushchishin, N.M., Kostak, Z.R. (2018). Bankіng system of Ukraine: current standard and future development. Ekonomika i suspil'stvo – Economy and society, 6, 783-789. Retrieved from economyandsociety. in. ua/journal/16_ukr/119. pdf [in Ukrainian].
3. Liang, D., Zhang, Y., Xu, Z., Jamaldeen, A. (2019). Pythagorean fuzzy VIKOR approaches based on TODIM for evaluating internet banking website quality of Ghanaian banking industry. Applied Soft Computing, 78, 583-594. doi.org/10.1016/j.asoc.2019.03.006
4. Affes, Z., Hentati-Kaffel, R. (2019). Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis. Computational Economics, 2019, 54: 1, 199-244. doi.org/10.1007/s10614-017-9698-0
5. Sharma, S. K. (2019). Integrating cognitive antecedents into TAM to explain mobile banking behavioral intention: A SEM-neural network modeling. Information Systems Frontiers, 21: 4, 815-827. doi.org/10.1007/s10796-017-9775-x
6. Li, Y., Allan, N., Evans, J.R. (2017). A Nonlinear Analysis of Operational Risk Events in Australian Banks. Journal of Operational Risk, Forthcoming. Retrieved from ssrn.com/abstract=2906327; doi.org/10.21314/JOP.2017.185
7. Saiti, B, Bacha, O.I., Masih, M. (2016). Testing the conventional and Islamic financial market contagion: evidence from wavelet analysis. Emerging Markets Finance and Trade, 52: 8, 1832-1849. doi.org/ 10.1080/1540496X.2015.1087784
8. Edurkar, A., Shaikh, A. A. (2018). Application of Morlet Wavelet Transform for analysis of Business Practices of Foreign Banks in India. Wealth: International Journal of Money, Banking & Finance, 7: 1, 12-17.
9. Okeke, C., Nwude, E.C. (2018). A Statistical Simulation for the Profitability of Banks: A Study. International Journal of Economics and Financial Issues, 8: 2, 243-254.
10. Affes, Z., Hentati-Kaffel, R. (2019). Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis. Computational Economics, 54: 1, 199-244. doi.org/10.1007/s10614-017-9698-0
11. Vasyurenko, O., Lyashenko, V., Podchesova, V. (2014). Efficiency of lending to natural persons and legal entities by banks of Ukraine: methodology of stochastic frontier analysis. Visnyk Natsional'noho banku Ukrainy – Herald of the National Bank of Ukraine, 1, 5-11 [in Ukrainian].
12. Anwar, M. (2019). Cost efficiency performance of Indonesian banks over the recovery period: A stochastic frontier analysis. The Social Science Journal, 56: 3, 377-389. doi.org/10.1016/j.soscij.2018.08.002
13. He, F., He, X. (2019). A Continuous Differentiable Wavelet Shrinkage Function for Economic Data Denoising. Computational Economics, 54: 2, 729-761. doi.org/10.1007/s10614-018-9849-y
14. Lyashenko, V., Deineko, Z., Ahmad, A. (2015). Properties of wavelet coefficients of self-similar time series. International Journal of Scientific and Engineering Research, 6: 1, 1492-1499. doi.org/10.14299/ijser.2015.01.025
15. Heil, C.E., Walnut, D.F. (1989). Continuous and discrete wavelet transforms. SIAM review, 31: 4, 628-666. doi.org/10.1137/1031129
16. Grinsted, A., Moore, J.C., Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11: 5/6, 561-566. doi.org/10.5194/npg-11-561-2004
17. Lyashenko, V., Zeleniy, O., Mustafa, S. K., Ahmad, M. A. (2019). An Advanced Methodology for Visualization of Changes in the Properties of a Dye. International Journal of Engineering and Advanced Technology, 9: 1, 7111-7114. doi.org/10.35940/ijeat.A1496.109119
Events calendar
M | T | W | T | F | S | S |
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |
7 | 8 | 9 | 10 | 11 | 12 | 13 |
14 | 15 | 16 | 17 | 18 | 19 | 20 |
21 | 22 | 23 | 24 | 25 | 26 | 27 |
28 | 29 | 30 | 31 |