IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i16p3507-d1216913.html
   My bibliography  Save this article

Reserve Fund Optimization Model for Digital Banking Transaction Risk with Extreme Value-at-Risk Constraints

Author

Listed:
  • Moch Panji Agung Saputra

    (Doctoral Mathematics Study Programme, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Diah Chaerani

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Sukono

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Mazlynda Md. Yusuf

    (Faculty of Science and Technology, Universiti Sains Islam Malaysia (USIM), Bandar Baru Nilai, Nilai 71800, Negeri Sembilan, Malaysia)

Abstract

The digitalization of bank data and financial operations creates a large risk of loss. Losses due to the risk of errors in the bank’s digital system need to be mitigated through the readiness of reserve funds. The determination of reserve funds needs to be optimized so that there is no large excess of reserve funds. Then the rest of the reserve fund allocation can be used as an investment fund by the bank to obtain additional returns or profits. This study aims to optimize the reserve fund allocation for digital banking transactions. In this case, the decision variable is value reserved based on potential loss of each digital banking, and the objective function is defined as minimizing reserve fund allocation. Furthermore, some conditions that become limitation are rules of Basel II, Basel III, and Article 71 paragraph 1 of the Limited Liability Company Law. Since the objective function can be expressed as a linear function, in this paper, linear programming optimization approach is thus employed considering Extreme Value-at-Risk (EVaR) constraints. In the use of EVaR approach in the digital banking problem, it is found that the loss meets the criteria of extreme data based on the Generalized Pareto Distribution (GPD). The strength of reserve funds using linear programming optimization with EVaR constraints is the consideration of potential losses from digital banking risks that are minimized so that the allocation of company funds becomes optimum. While the determination of reserve funds with a standard approach only considers historical profit data, this can result in excessive reserve funds because they are not considered potential risks in the future period. For the numerical experiment, the following risk data are used in the modeling, i.e., the result of a sample simulation of digital banking losses due to the risk of system downtime, system timeout, external failure, and operational user failure. Therefore, the optimization model with EVaR constraints produces an optimal reserve fund value, so that the allocation of bank reserve funds becomes efficient. This provides a view for banking companies to avoid the worst risk, namely collapse due to unbalanced mandatory reserve funds.

Suggested Citation

  • Moch Panji Agung Saputra & Diah Chaerani & Sukono & Mazlynda Md. Yusuf, 2023. "Reserve Fund Optimization Model for Digital Banking Transaction Risk with Extreme Value-at-Risk Constraints," Mathematics, MDPI, vol. 11(16), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3507-:d:1216913
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/16/3507/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/16/3507/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Francisco Zabala Aguayo & Beata Ślusarczyk, 2020. "Risks of Banking Services’ Digitalization: The Practice of Diversification and Sustainable Development Goals," Sustainability, MDPI, vol. 12(10), pages 1-10, May.
    2. Fabiani, Andrea & López-Piñeros, Martha & Peydró, José-Luis & Soto, Paul E., 2022. "Capital Controls, Domestic Macroprudential Policy and the Bank Lending Channel of Monetary Policy," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 139(November ), pages 1-1.
    3. Manfred Gilli & Evis këllezi, 2006. "An Application of Extreme Value Theory for Measuring Financial Risk," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 207-228, May.
    4. Ratna Mappanyuki & Min Sururi Anfusina & Syamsu Alam, 2023. "The Impact of Media Reputation, Customer Satisfaction, Digital Banking, and Risk Management Moderation on ROA," Studies in Media and Communication, Redfame publishing, vol. 11(4), pages 78-85, June.
    5. Mishra, Mayank, 2020. "Evolution of the invisible bank: How partnerships with FinTechs are driving digital innovation," Journal of Digital Banking, Henry Stewart Publications, vol. 5(1), pages 36-40, June.
    6. Moch Panji Agung Saputra & Sukono & Diah Chaerani, 2022. "Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method," Risks, MDPI, vol. 10(1), pages 1-18, January.
    7. Garth J. van Schalkwyk & Peter J. Witbooi, 2017. "A model for bank reserves versus treasuries under Basel III," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(2), pages 237-247, March.
    8. Xu Zhao & Zhongxian Zhang & Weihu Cheng & Pengyue Zhang, 2019. "A New Parameter Estimator for the Generalized Pareto Distribution under the Peaks over Threshold Framework," Mathematics, MDPI, vol. 7(5), pages 1-18, May.
    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. Carlin C. F. Chu & Simon S. W. Li, 2024. "A multiobjective optimization approach for threshold determination in extreme value analysis for financial time series," Computational Management Science, Springer, vol. 21(1), pages 1-14, June.
    2. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    3. Amira Dridi & Mohamed El Ghourabi & Mohamed Limam, 2012. "On monitoring financial stress index with extreme value theory," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 329-339, March.
    4. Gerlach, Richard & Wang, Chao, 2020. "Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 489-506.
    5. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 83-102, June.
    6. Nemanja Milanović & Miloš Milosavljević & Slađana Benković & Dušan Starčević & Željko Spasenić, 2020. "An Acceptance Approach for Novel Technologies in Car Insurance," Sustainability, MDPI, vol. 12(24), pages 1-15, December.
    7. Xia Yang & Jing Zhang & Wei-Xin Ren, 2018. "Threshold selection for extreme value estimation of vehicle load effect on bridges," International Journal of Distributed Sensor Networks, , vol. 14(2), pages 15501477187, February.
    8. Xin Chen & Zhangming Shan & Decai Tang & Biao Zhou & Valentina Boamah, 2023. "Interest rate risk of Chinese commercial banks based on the GARCH-EVT model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    9. Chao Wang & Richard Gerlach, 2019. "Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall," Papers 1906.09961, arXiv.org.
    10. Pejman Peykani & Mostafa Sargolzaei & Mohammad Hashem Botshekan & Camelia Oprean-Stan & Amir Takaloo, 2023. "Optimization of Asset and Liability Management of Banks with Minimum Possible Changes," Mathematics, MDPI, vol. 11(12), pages 1-24, June.
    11. Jian Zhou, 2012. "Extreme risk measures for REITs: a comparison among alternative methods," Applied Financial Economics, Taylor & Francis Journals, vol. 22(2), pages 113-126, January.
    12. Giuseppe Storti & Chao Wang, 2023. "Modeling uncertainty in financial tail risk: A forecast combination and weighted quantile approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1648-1663, November.
    13. Gonzales-Martínez, Rolando, 2008. "Medidas de Riesgo Financiero y una Aplicación a las Variaciones de Depósitos del Sistema Financiero Boliviano [Risk Measures and an Application to the Withdrawals of Deposits in the Bolivian Financ," MPRA Paper 14700, University Library of Munich, Germany.
    14. Andrea Fabiani & Martha López Piñeros & José-Luis Peydró & Paul E. Soto, 2021. "Capital controls, corporate debt and real effects," Economics Working Papers 1833, Department of Economics and Business, Universitat Pompeu Fabra.
    15. Mizgier, Kamil J. & Hora, Manpreet & Wagner, Stephan M. & Jüttner, Matthias P., 2015. "Managing operational disruptions through capital adequacy and process improvement," European Journal of Operational Research, Elsevier, vol. 245(1), pages 320-332.
    16. Salhi, Khaled & Deaconu, Madalina & Lejay, Antoine & Champagnat, Nicolas & Navet, Nicolas, 2016. "Regime switching model for financial data: Empirical risk analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 148-157.
    17. Martha López-Piñeros & Norberto Rodríguez-Niño & Miguel Sarmiento, 2022. "Monetary Policy and Portfolio Flows in an Emerging Market Economy," Borradores de Economia 1200, Banco de la Republica de Colombia.
    18. Bi, Guang & Giles, David E., 2009. "Modelling the financial risk associated with U.S. movie box office earnings," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2759-2766.
    19. Jian Zhou & Randy Anderson, 2012. "Extreme Risk Measures for International REIT Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 45(1), pages 152-170, June.
    20. Florian Diener & Miroslav Špaček, 2021. "Digital Transformation in Banking: A Managerial Perspective on Barriers to Change," Sustainability, MDPI, vol. 13(4), pages 1-27, February.

    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:jmathe:v:11:y:2023:i:16:p:3507-:d:1216913. 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 (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.