Year of Graduation
Analysis of the banks' group strategy using simulation modeling methods
Faculty of Business Informatics
State and private banks play an important role in the sphere of the credit and monetary policy of the government. Their activity can have a significant impact on the economic development of the country and its industries. As a result development of efficient methods and techniques for asset and liability management in banks as well as interest rate management, risk managementThe purpose of the research is to develop of a model to simulate the functioning of four independent banks on the Russian market and to find the effective strategy for each of them. In order to reach the stated goals it is necessary to fulfill the following tasks:· Analysis current approaches to asset and liability management in bank and modeling of their operations;· Development of the dynamic model including system of equations for banks operations description;· Development of simulation model on the basis of dynamic mode, created on the previous steps:· Conduct of numerical experiments on real data;· Optimization of the control parameters of the model and risk assessment using Monte-Carlo method;· Making recommendations on effective asset and liability strategy for each of bank in the model.Current research consists of four chapters:• The first chapter presents a brief description of the banking sector in Russia and also includes of the scientific and business literature, devoted to various method of strategic management in banks, simulation modeling methods, agent-based modeling and mathematical modeling of banks’ operations; • The second chapter includes the basic results of statistical data analysis, which was used to find dependency between various elements of banks activities. Also the chapter presents mathematical description of banks’ activities;• The final part is devoted to the development of simulation model using Powersim Studio and conducting practical experiments with the real data. Simulation experiments included: control parameters optimization, using genetic algorithms and risk analysis using Monte-Carlo methods. According to the experiments results the conclusions were drawn and recommendations on the banks asset and liability strategy were made.