| 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 ▼
| Article in English (pp. 32 - 44) | Download | Downloads :323 |
REFERENCES ▼
1. Vartsaba, V.І. (2018). Problems of securing financial stability of Ukrainian banking systems. Naukovyj visnyk Uzhhorods'koho universytetu. Seriia Ekonomika – Science News of Uzhgorod University. Seriya Ekonomika, 1: 51, 311-316. doi.org/10.24144/2409-6857.2018.1(51).311-316 [in Ukrainian].
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
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
© Institute for economics and forecasting 2024. All rights reserved
