IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v10y2022i9p213-d908016.html

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Alan K. Karaev & Oksana S. Gorlova & Vadim V. Ponkratov & Marina L. Sedova & Nataliya S. Shmigol & Margarita L. Vasyunina, 2022. "A Comparative Analysis of the Choice of Mother Wavelet Functions Affecting the Accuracy of Forecasts of Daily Balances in the Treasury Single Account," Economies, MDPI, vol. 10(9), pages 1-27, September.
    • Handle: RePEc:gam:jecomi:v:10:y:2022:i:9:p:213-:d:908016
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/10/9/213/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7099/10/9/213/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Kailei & Cheng, Jinhua & Yi, Jiahui, 2022. "Copper price forecasted by hybrid neural network with Bayesian Optimization and wavelet transform," Resources Policy, Elsevier, vol. 75(C).
    2. Ian Lienert, 2009. "Modernizing Cash Management," IMF Technical Notes and Manuals 09/03, International Monetary Fund.
    3. Israel Fainboim Yaker & Sailendra Pattanayak, 2010. "Treasury Single Account: Concept, Design and Implementation Issues," IMF Working Papers 2010/143, International Monetary Fund.
    4. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
    5. Mr. Ian Lienert, 2009. "Modernizing Cash Management," IMF Technical Notes and Manuals 2009/003, International Monetary Fund.
    6. Fry, John & Griguta, Vlad-Marius & Gerber, Luciano & Slater-Petty, Helen & Crockett, Keeley, 2021. "Modelling corporate bank accounts," Economics Letters, Elsevier, vol. 205(C).
    7. Izani Ibrahim & Kamilah Kamaludin & Sheela Sundarasen, 2020. "COVID-19, Government Response, and Market Volatility: Evidence from the Asia-Pacific Developed and Developing Markets," Economies, MDPI, vol. 8(4), pages 1-22, November.
    8. Xin Zhang & Donggyu Kim & Yazhen Wang, 2016. "Jump Variation Estimation with Noisy High Frequency Financial Data via Wavelets," Econometrics, MDPI, vol. 4(3), pages 1-26, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thoma Gatluak Reat & Anthony Duku Peter, 2023. "Impact of Implementing Treasury Single Accounts on Financial Performance of Public Institutions: Case of National Ministries, Departments, And Agencies," International Journal of Science and Business, IJSAB International, vol. 19(1), pages 49-69.
    2. Busharads, Mohamed Osman Elamin & Sam-Quarm, Richmond & Sam-Quarm, Richmond & Institute of Research, Asian, 2020. "The Ramifications of the Treasury Single Account, the Ifmis Platform, and Government Cash Management in Developing Economies in the Wake of the Covid-19 Pandemic: Ghana’s Empirical Example," OSF Preprints j9dcx, Center for Open Science.
    3. Thordur Jonasson & Mr. Michael G. Papaioannou, 2018. "A Primer on Managing Sovereign Debt-Portfolio Risks," IMF Working Papers 2018/074, International Monetary Fund.
    4. Nataliia REKOVA & Iryna DOLOZINA & Vitalii NITSENKO & Yurii ZAITSEV & Victor ZAMLYNSKYI, 2018. "Budgetary Revenue Structure At Central Level Of Public Administration In The Federal Countries," REVISTA ADMINISTRATIE SI MANAGEMENT PUBLIC, Faculty of Administration and Public Management, Academy of Economic Studies, Bucharest, Romania, vol. 2018(30), pages 37-50, June.
    5. Michelle L. Lofton & Mikhail Ivonchyk, 2022. "Financial manager professionalism and use of interfund transfers: Evidence from Georgia counties," Public Budgeting & Finance, Wiley Blackwell, vol. 42(2), pages 171-195, June.
    6. Zivanai Mazhambe & Nzovwa Mwela Chomba, 2024. "An Evaluation of the Implementation of the Treasury Single Account System in Zambia: A Case Study of Selected Government Ministries," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 13(4), pages 42-51, April.
    7. Israel Fainboim Yaker & Sailendra Pattanayak, 2010. "Treasury Single Account: Concept, Design and Implementation Issues," IMF Working Papers 2010/143, International Monetary Fund.
    8. Zhang, Yongmin & Sun, Yiru, 2023. "Did U.S. and Chinese investors respond differently to the exogenous shocks from COVID-19 and the war in Ukraine?," International Review of Financial Analysis, Elsevier, vol. 88(C).
    9. Li, Ning & Li, Jiaojiao & Wang, Qizhou & Yan, Dairong & Wang, Liguan & Jia, Mingtao, 2024. "A novel copper price forecasting ensemble method using adversarial interpretive structural model and sparrow search algorithm," Resources Policy, Elsevier, vol. 91(C).
    10. Afzal, Sadegh & Ziapour, Behrooz M. & Shokri, Afshar & Shakibi, Hamid & Sobhani, Behnam, 2023. "Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms," Energy, Elsevier, vol. 282(C).
    11. Ozdemir, Ali Can & Buluş, Kurtuluş & Zor, Kasım, 2022. "Medium- to long-term nickel price forecasting using LSTM and GRU networks," Resources Policy, Elsevier, vol. 78(C).
    12. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & Jesus Lopez-Sotelo & David Celeita, 2023. "An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture," Energies, MDPI, vol. 16(19), pages 1-24, September.
    13. Vicente E. Montano, 2024. "Is there Price Distortion in the Philippine Rice Market: A Bayesian Discrete Wavelet Transform Analysis," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(15), pages 275-287, December.
    14. Zhou, Jianguo & Xu, Zhongtian, 2023. "Optimal sizing design and integrated cost-benefit assessment of stand-alone microgrid system with different energy storage employing chameleon swarm algorithm: A rural case in Northeast China," Renewable Energy, Elsevier, vol. 202(C), pages 1110-1137.
    15. Bakry, Walid & Kavalmthara, Peter John & Saverimuttu, Vivienne & Liu, Yiyang & Cyril, Sajan, 2022. "Response of stock market volatility to COVID-19 announcements and stringency measures: A comparison of developed and emerging markets," Finance Research Letters, Elsevier, vol. 46(PA).
    16. Akinleye G. T & Fajuyagbe S. B & Owoniya B. O, 2018. "Treasury Single Account: A Nudge towards Public Sector Accountability in Nigeria," Journal of Economics and Behavioral Studies, AMH International, vol. 10(6), pages 201-210.
    17. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    18. Shengxiang Lv & Lin Wang & Sirui Wang, 2023. "A Hybrid Neural Network Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(4), pages 1-18, February.
    19. Mengmeng Zhang & Yi Tang & Junzhu Liu & Zhoupeng Chen & Qing Kang, 2025. "Increase or Decrease? The Impact of Land Development Rights Transfer on Regional Carbon Emission Governance," Sustainability, MDPI, vol. 17(7), pages 1-20, March.
    20. Grossman, Irina & Wilson, Tom & Temple, Jeromey, 2023. "Forecasting small area populations with long short-term memory networks," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jecomi:v:10:y:2022:i:9:p:213-:d:908016. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.