Recibido (22/05/2020) Revisado (14/02/2021) Aceptado (07/05/2021) RESUMEN: Entender y predecir el fenómeno inflacionario es un problema central para los economistas y agentes tomadores de decisiones. Tradicionalmente se han utilizado técnicas econométricas de series de tiempo para estudiar este fenómeno; pero, ¿puede la economía de la complejidad aportar una visión complementaria a los estudios anteriores?

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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 gradient adjustment process is used to create a dynamic model of banking loan. The sign of the loan’s marginal profit determines how much money will be loaned in the future. In this research, using bifurcation theory, we investigate the cost of loan in the dynamics of a bank’s loan. The results of the analysis indicate that the stability of the loan equilibrium might be impacted by the cost of loan. Loan equilibrium may become unstable through transcritical bifurcation if the cost of the loan is sufficiently high. The loan equilibrium may become unstable through flip bifurcation and path to chaos, however, if the cost of loan is too low. If the cost of loan lies between the bifurcation values, the loan equilibrium is stable. The numerical simulations back up these conclusions. Additionally, we display the Lyapunov exponent graph, which shows the presence of chaos, and the chaotic loan graph, which is sensitive to the initial condition.

This research investigates a model of the spread of COVID-19 in Indonesia by paying attention to comorbid disease, self-quarantine, government-provided quarantine, and vaccination factors. The symmetrical aspects of the model are studied. The evaluation of the model reveals non-endemic and endemic equilibrium points and the basic reproduction number (BRN). We provide the local and global stability analysis of the equilibriums. According to the sensitivity analysis of the BRN, the key parameters impacting the spread of COVID-19 are the susceptible recruitment rate, contact rate, infection death rate, and probability of infected individuals having no comorbidities. In addition, we provide a sensitivity analysis to examine the effect of parameter changes in each subpopulation. We discovered that the natural death rate is the most sensitive parameter based on the sensitivity index after reaching equilibrium. Symmetry aspects appear in some of the visualizations of the model’s solution and the sensitivity of the BRN and parameters.