An Algorithm for Simulating the Banking Network System and Its Application for Analyzing Macroprudential Policy

Abstract

Modeling banking systems using a network approach has received growing attention in recent years. One of the notable models is that developed by Iori et al, who proposed a banking system model for analyzing systemic risks in interbank networks. The model is built based on the simple dynamics of several bank balance sheet variables such as deposit, equity, loan, liquid asset, and interbank lending (or borrowing) in the form of difference equations. Each bank faces random shocks in deposits and loans. The balance sheet is updated at the beginning or end of each period. In the model, banks are grouped into either potential lenders or borrowers. The potential borrowers are those that have lack of liquidity and the potential lenders are those which have excess liquids after dividend payment and channeling new investment. The borrowers and the lenders are connected through the interbank market. Those borrowers have some percentage of linkage to random potential lenders for borrowing funds to maintain their safety net of the liquidity. If the demand for borrowing funds can meet the supply of excess liquids, then the borrower bank survives. If not, they are deemed to be in default and will be removed from the banking system. However, in their paper, most part of the interbank borrowing-lending mechanism is described qualitatively rather than by detailed mathematical or computational analysis. Therefore, in this paper, we enhance the mathematical parts of borrowing-lending in the interbank market and present an algorithm for simulating the model. We also perform some simulations to analyze the effects of the model's parameters on banking stability using the number of surviving banks as the measure. We apply this technique to analyze the effects of a macroprudential policy called loan-to-deposit ratio based reserve requirement for banking stability.

The Role of Cost of Loan in Banking Loan Dynamics: Bifurcation and Chaos Analysis

“…This study uses a very straightforward model of a single bank with a simple balance sheet structure. In order to handle specific banking policies, such as macroprudential policy, which focuses on limiting the growth of banking loans, the model can be generated into a more general model with more balance sheet components [29], [30]. The approach can also be used to banking data, as shown in [31], [32], and other future publications.…”

Analysis of Banking Deposit Cost in the Dynamics of Loan: Bifurcation and Chaos Perspectives

“…The model in this paper is a very simple model of one bank that has a simple balance sheet structure. Thus, the model can be generated into a more general model which consists of more balance sheet components or to address some banking policies such as macroprudential policy that focuses on controlling the growth of banking loans [30], [31]. Other future works can also be done by implementing the model on banking data such as in [32], [33].…”

Stability and Sensitivity Analysis of the COVID-19 Spread with Comorbid Diseases

“…A sensitivity analysis is performed to examine the effect of changes in the model's parameters to the dynamics of the model's compartments. Some papers have used this sensitivity technique in several research areas [36][37][38]. Suppose we have a vector of variables X = (S, I h , I c , Q h , Q H , R) T , vector parameters P = (Λ, p, β, ν, p 1 , µ, α, d 1 , δ, γ, d 2 , η) T , and a vector of the right side of system (1) denoted by F = (F , G, H, I, J , K) T , where dS/dt = F , dI h /dt = G, dI c /dt = H, dQ h /dt = I, dQ H /dt = J , dR/dt = K.…”