Suscripción a Biblioteca: Guest
Portal Digitalde Biblioteca Digital eLibros Revistas Referencias y Libros de Ponencias Colecciones
Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Imprimir: 1064-2315
ISSN En Línea: 2163-9337

Volumes:
Volumen 51, 2019 Volumen 50, 2018 Volumen 49, 2017 Volumen 48, 2016 Volumen 47, 2015 Volumen 46, 2014 Volumen 45, 2013 Volumen 44, 2012 Volumen 43, 2011 Volumen 42, 2010 Volumen 41, 2009 Volumen 40, 2008 Volumen 39, 2007 Volumen 38, 2006 Volumen 37, 2005 Volumen 36, 2004 Volumen 35, 2003 Volumen 34, 2002 Volumen 33, 2001 Volumen 32, 2000 Volumen 31, 1999 Volumen 30, 1998 Volumen 29, 1997 Volumen 28, 1996

Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v51.i8.20
pages 16-30

Bank − Complex System

Valeriy A. Velichkin
University of Customs and Finance, Dnipro
Marina V. Timoshenko
University of Customs and Finance, Dnipro

SINOPSIS

The bank is represented as a complex financial and economic system with a certain state column vector. The parameters of an arbitrary state as a column vectors are defined as the product of the transition matrix from one state to another. This representation of the bank allows one to make the transition to statistical modeling of the bank's activities via the statistics of the transition matrix. When analyzing the activities of a bank, the most common tool used by researchers is the method of indicators, that is, the relation of financial and economic aggregates. With this approach, the evaluation of the bank's activities is carried out using various absolute and relative indicators. A comprehensive study of these indicators allows one to conclude about the effectiveness of the financial and economic activities of the bank. One should mention the method of comparing the actual state of the values of the studied indicators with the standard values, values of the past period, and values of the average level. The purpose of the research is to study the relationship between the parameters describing the state of the bank at a control instant of time from the parameters of the initial state and, on the basis of this dependence to develop methods for managing financial resources under the conditions of specified constraints. The concept of a transition matrix from one bank state to an arbitrary state is introduced. The transition of a bank as a complex FES (financial economic system) from one state to another occurs under the influence of the flow of finance consisting of elements − payments, the size of which may be equal to the minimum monetary unit. In general, each flow from payments, regardless of the value, transfers the system from one state to another. One banking (operational) day is selected as the time sampling. Consequently, a bank is considered as a complex financial and economic system with a discrete number of states, and one banking (operational) day is selected as a time unit. This corresponds to the current approach to financial management in the banking system. The practice of applying the method to the problem of asset liquidity indicators and the Monte Carlo method showed the effectiveness of the proposed model. The general conclusions of the work are as follows: the bank is represented as a complex dynamic financial and economic system with an numerous number of states; further development of the application of a system approach to financial management, the bank is presented in the form of a system consisting of a specific set of parameters, financial management is carried out through managing these parameters; the dependence of the parameters describing the bank factors under study at the control instant of time from the parameters of the initial state has been revealed; systematized payment flows, which are represented by the transition matrix, by breaking them into components, the quantitative influence of these components on the system parameters has been determined; it is proved that the relationship between the parameters of the control and initial states with a sufficient degree of probability can be expressed by a linear operator, the proposed formulas allow one to calculate all the components of a linear operator. On the basis of the obtained dependence of the parameters of the control state on the parameters of the initial state, methodical recommendations were developed for managing the parameters (assets and liabilities) of the banking structure.

REFERENCIAS

  1. Abidoval.K., Features of the functioning of a commercial bank as a system, Novyye tekhnologii, 2009, No. 2, 15-22. .

  2. Bakaev A.A., Kostina N.I., Yarovitsky N.V., Simulation models in economics [in Russian], Naukova dumka, Kiev, 1978. .

  3. Berezin A.A., Finogeev A.G., Mathematical modeling of change dynamics of bank indicators in the process of evolutionary development of the organizational field, www.naukovedenie.ru/09EVNl 16.pdf. .

  4. Bizyanov E.E., VoloshinM.V., The concept of multiagent modeling of the effectiveness of the functioning of information systems, Sovremennyye nauchnyye innovatsii, 2007. No. 1 (69), 18-25. .

  5. Buslenko N.P., Modeling of complex systems [in Russian], Nauka, Moscow, 1968. .

  6. Vasilieva E.E., Modeling a comprehensive assessment of the credit risk of banking [in Russian], PNIPU, Perm, 2017. .

  7. Voloshin I.V., Payments flows analysis of a commercial bank, Operatsionnoye upravlenie i strategicheskiy menedzhment v kommercheskom banke, 2002, No. 4, 78-85. .

  8. Voloshin I.V., Voloshina Ya.A., Solution of the "liquidity-income" dilemma for bank resources with a lognormal distribution, Biznes i banki, 2008, No. 10, 56-65. .

  9. Gribov A.F., Banking modeling [in Russian], Izdatelstvo Rossiyskoy ekonomicheskoy akademii im. G.V.Plekhanova, Moscow, 2004. .

  10. Evsyukov V.V., Trutnev D.N., Simulation in the liquidity risk management system of a commercial bank, Trudy Tulskogo GU, Seriya Ekonomika, 2009, No. 2, 18-30. .

  11. Evsyukov V.V., Fundamentals of mathematical modeling of financial processes [in Russian], Izdatelstvo Tulskogo GU, Tula, 2007. .

  12. KobelevN.B., Fundamentals of simulation of complex economic systems [in Russian], Delo, Moscow, 2003. .

  13. KostinaN.I., Alekseev A.A., Financial forecasting in economic systems [in Russian], Uniti-Dana, Moscow, 2002. .

  14. Kostina N.I., Suchok S.V., Automated models of credit risk, Bankovskie tekhnologii, 2003, No. 7-8. .

  15. Kostina N.I., Suchok S.V., Modeling the activities of a commercial bank in an economic crisis, Nauchnyi vestnik NADPSU, 2009, No. 1 (69), 79-82. .

  16. Kulikov Yu.S., Orlenko N.V., Velichkin V.A., Reservation: problems and methods of their solution, Bankovskoye delo, 1998, No. 1, 38-40. .

  17. Kulikov Yu.S., Orlenko N.V., Velichkin V.A., Some models of analysis of the financial condition of a commercial bank [in Russian], Dep. at the State Scientific and Technical Library of Ukraine 26/11/96, No. 2273, 1997, No. 2. .

  18. Kulikov Yu.S., VelichkinV.A., Orlenko N.V., Methodology for assessing the effectiveness of financial management of a commercial bank, Dep. at the State Scientific and Technical Library of Ukraine 26/11/96, No. 2272 Uk96, 1997, No. 2. .

  19. Moiseev N.N., Lectures on the theory of complex dynamic systems [in Ukrainian], Naukova dumka, Kiev, 1981. .

  20. Orlenko N.V., Kulikov Yu.S., Velichkin V.A., Bank reserves management model, Biznes-inform, 1998, No. 20, 63-64. .

  21. Prokhorov A.V., Strashnenko Yu.N., Interaction of simulation model agents in solving problems of managing financial resources of a bank, Vostochno-Yevropeyskiy zhurnal peredovykh tekhnologiy, 2011, No. 1, 3-15. .

  22. Rudakova N.V., Credit risk management of a commercial bank [in Russian], Ph.D., TNU, Tula, 2012, .

  23. Forrester J., World dynamics [Russian translation], Izdatelstvo AST, Moscow, 2003. .

  24. Ahmed S., Use of transition matrices in risk management and valuation, A Fair Isaac White Paper, 2004. .

  25. Bank for International Settlements: The New Basel Capital Accord, http://www.bis.org/publ/ bcbsca.htm. .

  26. Deventer D. van., Imai K., Credit risk models and the Basel Accords: the Merton model and reduced form models, John Wiley & Sons, New York, 2003. .

  27. GavalasD., Syriopoulos T., Bank credit risk management and rating migration analysis on the business, Int. J. Financial Stud., 2014, 2, 122-143. .

  28. Inamura Y., Estimating continuous time transition matrices from discretely observed data, Bank of Japan working paper series, 2006. .

  29. Israel R.B., Finding generators for Markov chains via empirical transition matrices, with applications to credit ratings, Mathematical Finance, 2001, 11(2), 245-265. .

  30. JafryY., Schuermann T., Metrics for comparing credit migration matrices, Wharton Financial Institutions Center Working paper N 03-09. Available at SSRN, 2003, http://ssrn.com/abstract=394020, DOI: 10.2139/ssm.394020. .

  31. Lando D., Skodebcrg T., Analyzing ratings transitions and rating drift with continuous observations, Journal of Banking & Finance, 2002, 26 (2/3), 423-444. .

  32. MuliamanD. Hadad, WimbohS., Rating migration matrices: empirical evidence in Indonesia, IFC Bulletin, 2009, No. 31, July, Available at SSRN, https://www.bis.org/ifc/publ/ifcb31.htm. .

  33. Metropolis N., UlamS., The Monte Carlo Method, J. Amer. statistical assoc., 1949, 44, No. 247, 335-341. .

  34. Rorres C, Anton H., Elementary linear algebra, John Wiley & Sons, New Jersey, 1987. .

  35. Sinkey J.F., Commercial bank financial management, Macmillan and Collier Macmillan, London, New York, 1986. .