Money, finances and credit

Analysis of the banking system incoming financial flows dynamics based at their time series’ wavelet-transformation

Economic Annals-XXI

Economic Annals-ХХI: Volume 153, Issue 7-8(2), Pages: 44-48

Citation information:
Pohorelenko, N. (2015). Analysis of the banking system incoming financial flows dynamics based at their time series’ wavelet-transformation. Economic Annals-XXI, 7-8(2), 44-48. https://ea21journal.world/index.php/ea-v153-11/

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Nataliia Pohorelenko
PhD (Economics),
Associate Professor,
Kharkiv Institute of Banking of University of Banking of the National Bank of Ukraine
55 Peremohy Ave, Kharkiv, 61174, Ukraine
pogorelenko@inbox.ru

Analysis of the banking system incoming financial flows dynamics based at their time series’ wavelet-transformation

Abstract. Introduction. Among the existing multitude of various financial flows of the banking system, input streams represent bank customers’ funds at their deposit accounts. They define the substantiveness of the banking system resource potential and can be considered as a potential source of investments into the economy. Purpose. To identify and generalize the dynamics of incoming financial flows of the domestic banking system on the basis of the wavelet-analysis methodology. Results. The importance of the study of financial flows the dynamics in the banking system using wavelet-analysis methodology is emphasized. The expediency of simultaneous wavelet-analysis for aggregate incoming financial flow of the banking system as well as its individual components has been grounded. It is pointed out that such approach allows discovering indications of reciprocity between the studied rows of data in a fuller way. For the analysis the period of time from January, 2007 to May, 2015 was chosen. In particular, the presence of more significant correlations between the analyzed data rows during the economic crises in Ukraine was established. The importance of the results of classical methods of statistical analysis and wavelet-analysis was outlined as it is effective in making more balanced and reasonable decisions on banking system regulation. Conclusions. It is noted that the correlation value between the rows of data that reflect the dynamics of the banking system’s deposits and their individual structural components during the economic crisis in Ukraine is much higher than in other periods. This allows us to make conclusions regarding reasonable adjustment of the component dynamics of the total number of deposits and create a balanced banking policy.

Keywords: Deposits; Financial Flows; Banking System; Nonfinancial Corporations; Households; Time Series; Wavelet Analysis

JEL Classification: С65; E50 и Е59; G21

Reference

  1. Andrushkiv, T. (2012). Deposit policy of commercial banks and areas of improvement. Ukrainska nauka: munyle, sychasne, maibutne (Ukrainian Science: Past Present and Future), 17, 3-13 (in Ukr.).
  2. Versal, N. (2009). Features of formation of deposit banks resources. Fіnansy Ukrainy (Finance of Ukraine), 12, 89-95 (in Ukr.).
  3. Lutsiv, B., & Zaslavskaya, O. (2012). Features resource support commercial banks Ukraine. Visnyk TNEU (Herald of TNEU), 2, 89-102 (in Ukr.).
  4. Karcheva, G. (2009). Features of the banking system of Ukraine in conditions of financial crisis. Visnyk Natsionalnoho banku Ukrainy (Herald of the National Bank of Ukraine), 11, 10-16 (in Ukr.).
  5. Vozhzhov, А. P. (2002). Demand deposits in the formation of a stable and controlled banks reserves. Visnyk Natsionalnoho banku Ukrainy (Herald of the National Bank of Ukraine), 11, 5-8 (in Ukr.).
  6. Dobrovolskaya, I. A. (2013). Interrelations of banking sectors of European economies as reflected in separate indicators of the dynamics of their cash flows influencing the formation of the resource potential of banks. European Applied Sciences, 1(2), 114-118.
  7. Lyashenko, V. V. (2015). Properties of wavelet coefficients of self-similar time series. International Journal of Scientific and Engineering Research, 6(1), 1492-1499.
  8. Delbeke, L. (2008). Stochastic integral representation and properties of the wavelet coefficients of linear fractional stable motion. Stochastic Processes and their Application, 86(2), 177-182.
  9. Aguiar-Conraria, L., & Soares, M. J. (2011). Oil and the macroeconomy: using wavelets to analyze old issues. Empirical Economics, 40(3), 645-655.
  10. Benhmad, F. (2012). Modeling nonlinear Granger causality between the oil price and US dollar: A wavelet based approach. Economic Modelling, 29(4), 1505-1514.
  11. Fan, Y., & Gencay, R. (2010). Unit root tests with wavelets. Econometric Theory, 26(5), 1305-1331.
  12. Jammazi, R., Lahiani, A., & Nguyen, D. K. (2015). A wavelet-based nonlinear ARDL model for assessing the exchange rate pass-through to crude oil prices. Journal of International Financial Markets, Institutions and Money, 34, 173-187.
  13. Grinsted, A. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear processes in geophysics, 11(5-6), 561-566.
  14. Torrence, C. (1999). Interdecadal changes in the ENSO-monsoon system. Journal of Climate, 12(8), 2679-2690.

Received 20.08.2015

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