ZNAČAJ PRIMENE STATISTIČKIH MODELA U RAZVOJU REJTING SISTEMA FINANSIJSKIH INSTITUCIJA

Autori

  • Slobodan Šegrt Fakultet za poslovne studije i pravo, Univerzitet “Union - Nikola Tesla", Beograd, Republika Srbija Author
  • Aca Ranđelović Fakultet za poslovne studije i pravo, Univerzitet “Union - Nikola Tesla", Beograd, Republika Srbija Author
  • Dejan Đurić Ministarstvo odbrane, Beograd, Republika Srbija Author

DOI:

https://doi.org/10.59864/Oditor62402SS

Ključne reči:

verovatnoća neplaćanja, dužnik, modeli, interni rejting, statistička analiza, adekvatnost bankarskog kapitala

Apstrakt

Najbitniji sistem za upravljanje rizicima i merenje finansijski performansi jeste rejting sistem. Rejting sistem se smatra integralnim delom bankarskih tekućih poslova i njihove kulture upravljanja rizicima. Sve banke se trude da istaknu tu njegovu pripadnost tekućim poslovima, kako bi pokazali supervizorima neophodnost njegovog korišćenja u svrhu determinisanja zahteva minimalnog regulatornog kapitala.

Komitet u Bazelu za pojedine klase izloženosti rizicima, preporučuje korišćenje osnovne metodologije kod koje bankarske institucije kao ulaznu veličinu koriste sopstvenu procenu rizika neplaćanja dužnika, dok se procene dodatnih faktora rizika primenjuju putem standardizovonih pravila supervizora.

Ova osnovna metodologija je dostupna za bankarske institucije koje imaju mogućnost da svoje supervizore uvere da su one sposobne da odgovore na određene minimalne zahteve bankarskog sistema, proces upravljanja rizika i sposobnost procene njegovih bitnih komponenti.

Takođe pored osnovne metodologije definisane su i napredne metodologije koje pružaju mogućnost unutrašnje procene komponenti rizika. Široka primena navedenih procena je važan deo dinamičkog i risk-senzitivnog IRB  pristupa (Internal Rating Based). Tako mogu da se identifikuju i razlikuju one bankarske institucije koje imaju sposobnost da sprovedu određenu validnu i kvantifikovanu procenu rizika.

Neki određeni modeli, postupci i procesi procene verovatnoće neplaćanja u određenim situacijama, putem dobijenih rezultata, omogućuju menadžmentu bankarskih institucija detaljno analiziranje realne slike mogućih dužnika što u krajnjem pruža i moguću bolju analizu njihovog difolta.

##plugins.themes.default.displayStats.downloads##

##plugins.themes.default.displayStats.noStats##

Reference

Abrahams, T.O., Ewuga, S.K., Kaggwa, S., Uwaoma, P.U., Hassan, A.O., & Dawodu, S.O., (2024), Mastering compliance: a comprehensive review of regulatory frameworks in accounting and cybersecurity, Computer Science & IT Research Journal, 5(1), 120-140.

Abrahams, T.O., Farayola, O.A., Kaggwa, S., Uwaoma, P.U., Hassan, A.O., & Dawodu, S.O., (2024a), Reviewing third-party risk management: best practices in accounting and cybersecurity for superannuation organizations, Finance & Accounting Research Journal, 6(1), 21-39.

Alastair L., (2022), Masterning Risk Modeling, First Edition, London, England,

Alirezaie, M., Hoffman, W., Zabihi, P., Rahnama, H., & Pentland, A., (2024), Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing, Journal of Risk and Financial Management, 17(1), 38.

Altman, E., & Sabato, G., (2007), Modeling Credit Risk for SMEs: Evidence from the US Market, Abacus, 43 (3), 323-357.

BCBS., (2006), International Convergence of Capital Measurement and Capital Standards: A Revised Framework, Basel Committee on Banking Supervision.

Bessis J, (2019), Risc Management in Banking, Amacom, London,

Brealey R., (2021), Principles of Corporate Finance, Mc Grew-Hill, New Jork,

Crook, J.N., Edelman, D.B., & Thomas, L.C., (2007), Recent developments in consumer credit risk assessment, European Journal of Operational Research, 183 (3), 1447-1465

Daníelsson, J., & Macrae, R., (2021), Uthemann, A., Artificial intelligence and systemic risk, Journal of Banking & Finance. https://doi.org/10.1016/j.jbankfin.2021.106290

Dowling E.T., (2017), Mathematical Methods for Business and Economics”, McGrow Hill, New York.

Engeimann B., Rauhmeier R.,(2018), The Basel II Risk Parameters Estimation, Validation and Stress Testing, Dresdner Bank, Berlin

Genest, B., & Brie, L., (2013), Basel II IRB Risk Weight Functions: Demonstration and Analysis, Global Research & Analytics by Chappuis Halder & Cie .

Gitman L., (2019), Principles of Managerial Finance, Harper Collins Publishers, New Jork,

Greuning H., (2018), Analyzing and Managing Banking risk, Second Edition, The World Bank,

Hunt, W., Marshall, K., & Perry, R., (2020), Artificial Intelligence's Role in Finance and How Financial Companies are Leveraging the Technology to Their Advantage, Thesis. https://doi.org/10.13140/RG.2.2.31982.64328

Matz L., (2017), Liquidity Risk Measurement and Management, John Wiley Sons,

Mishkin F., (2020), Banking and Financijal Market, Third edition, Collins Publishers,

Ochuba, N.A., Usman, F.O., Amoo, O.O., Okafor, E.S., & Akinrinola, O., (2024), Innovations in business models through strategic analytics and management: conceptual exploration for sustainable growth, International Journal of Management & Entrepreneurship Research, 6(3), 554-566.

Samuels J., (2016), Management of company Finance, Champan&hall, London

Zhou, W., Yan, Z., & Zhang, L., (2024), A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction, Scientific Reports, 14(1), 5905.

##submission.downloads##

Objavljeno

2024-09-07

Broj časopisa

Rubrika

Articles

##plugins.generic.recommendBySimilarity.heading##

##common.pagination##

##plugins.generic.recommendBySimilarity.advancedSearchIntro##