A Comparative Analysis of the Choice of Mother Wavelet Functions Affecting the Accuracy of Forecasts of Daily Balances in the Treasury Single Account

Alan K. Karaev, Oksana S. Gorlova, Vadim V. Ponkratov (), Marina L. Sedova, Nataliya S. Shmigol and Margarita L. Vasyunina
Additional contact information
Alan K. Karaev: Department of Public Finance, Faculty of Finance, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia
Oksana S. Gorlova: Department of Public Finance, Faculty of Finance, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia
Vadim V. Ponkratov: Department of Public Finance, Faculty of Finance, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia
Marina L. Sedova: Department of Public Finance, Faculty of Finance, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia
Nataliya S. Shmigol: Department of Public Finance, Faculty of Finance, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia
Margarita L. Vasyunina: Department of Public Finance, Faculty of Finance, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia

Economies, 2022, vol. 10, issue 9, 1-27

Abstract: Improving the accuracy of cash flow forecasting in the TSA is key to fulfilling government payment obligations, minimizing the cost of maintaining the cash reserve, providing the absence of outstanding debt accumulation and ensuring investment in financial instruments to obtain additional income. This study aims to improve the accuracy of traditional methods of forecasting the time series compiled from the daily remaining balances in the TSAbased on prior decomposition using a discrete wavelet transform. The paper compares the influence of selecting a mother wavelet out of 570 mother wavelet functions belonging to 10 wavelet families (Haar;Dabeshies; Symlet; Coiflet; Biorthogonal Spline; Reverse Biorthogonal Spline; Meyer; Shannon; Battle-Lemarie; and Cohen–Daubechies–Feauveau) and the decomposition level (from 1 to 8) on the forecast accuracy of time series compiled from the daily remaining balances in the TSA in comparison with the traditional forecasting method without prior timeseries decomposition. The model with prior time series decomposition based on the Reverse Biorthogonal Spline Wavelet [5.5] mother wavelet function, upon the eighth iteration, features the highest accuracy, significantly higher than that of the traditional forecasting models. The choice of the mother wavelet and the decomposition level play an important role in increasing the accuracy of forecasting the daily remaining balances in the TSA.

Keywords: wavelet analysis; discrete wavelet transform; treasury single account; balances; time series forecasting (search for similar items in EconPapers)
JEL-codes: E F I J O Q (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7099/10/9/213/pdf (application/pdf)
https://www.mdpi.com/2227-7099/10/9/213/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jecomi:v:10:y:2022:i:9:p:213-:d:908016

Access Statistics for this article

Economies is currently edited by Ms. Hongyan Zhang

More articles in Economies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().