Loan Demand Research Articles

The Oil Obsession

Oil Money recounts the evolution in U.S. relations with the petroleum-exporting monarchies of the Persian Gulf following the mid-1970s “oil revolution.” David Wight details the ways in which principals in the Nixon, Ford, and Carter presidential administrations fretted about while ultimately adjusting to and, arguably, gaining from the new order of higher oil prices, the staged takeover of the western firms’ operations, and the vastly increased rents (“petrodollars”) that royals and revolutionaries now commanded. He handles a complex story dexterously. He makes great use of Treasury Department records, and, while concentrating on the Persian Gulf producers, takes us on a side-trip to mid-1970s Cairo where the Americans dreamed of petrodollar investments playing a lead role in the liberalization of the Egyptian economy. U.S. Secretary of the Treasury William Simon sold the Saudis and Kuwaitis on investing in Treasury securities. U.S. (and European) bankers chased after deposits to meet the insatiable global demand for loans to pay for oil. President Nixon and Crown Prince Fahd signed wide-ranging agreements in 1974 in support of Saudi Arabia’s defense and development, thus launching another so-called “special relationship.” Then there were the weapons. Arms exports to Saudi Arabia and Iran, which began in earnest in the 1960s, grew exponentially after 1973 as part of Nixon’s post-Vietnam era “twin pillars” strategy, where the two allies were to assume a greater share of the region’s security burden, soaking up more of those petrodollars in the meantime. American architects, bankers, contractors, crooks, engineers, and Fortune 500 executives would descend on the domains of the Al Saud in unprecedented numbers. Container ships loaded with building materials, cars, and consumer goods clogged the kingdom’s harbors, while the capital, Riyadh, expanded at dizzying speed.

Heterogenous impact of monetary policy on the Philippine rural banking system

This paper shows the differential impact of monetary policy on the lending behavior of rural banks, with the bank lending channel being operational in small rural banks. While big rural banks are able to protect their lending portfolio from contractionary monetary policy by the size of their balance sheet, small rural banks with less diversified funding portfolio cannot. Moreover, highly capitalized rural banks are more inclined to protect their capital than expand their lending portfolio, following monetary tightening and higher capital requirement. The insignificance of gross domestic product (GDP) growth may reflect weakness in effective loan demand and lack of diversification that could have also impinged on the earning capacity of rural banks, as supported by initial estimates on the drivers of rural bank profitability. The finding on heterogeneous effects of monetary policy on rural banks has a secondary implication of lending credence to the principle of proportionality embodied in the BSP’s bank regulatory framework.

A study on personal credit risk assessment model based on integrated learning

Financial risk control is an important means to control capital security. Under the background of market economy and inclusive finance, the demand for personal credit loans is increasing rapidly. Therefore, it is very important to control financial risks. However, in the financial field, due to the requirements of security, data is scarce and large-scale data cannot be obtained, so many researchers cannot carry out detailed data analysis and technical research, and can only carry out simple theoretical analysis. However, the current financial risk control mostly relies on manual operation, which is inefficient. In order to solve this problem, this paper firstly obtains a large number of personal credit loan information from foreign websites. Then, the feature engineering technology is used to clean the data. Finally, the data mining technology in the field of artificial intelligence is used for improvement, which greatly promotes the automation and intelligent process of financial risk control. In this paper, the data mining algorithm is used to classify the credit loan dataset. The results show that our model achieves good results and can effectively avoid nearly 70% default risk, and the time cost is very low.

Exploitation of Machine Learning Algorithms for Detecting Financial Crimes Based on Customers’ Behavior

Longer-term projections indicate that today’s developing and rising nations will account for roughly 60% of the global GDP by 2030. There is tremendous financial growth and advancement in developing countries, resulting in a high demand for personal loans from citizens. Depending on their needs, many people seek personal loans from banks. However, it is difficult for banks to predict which consumers will pay their bills and which will not since the number of bank frauds in many countries, notably India, is growing. According to the Reserve Bank of India, the Indian banking industry uncovered INR 71,500 in the scam in the fiscal year 2018–2019. The average lag time between the date of the occurrence and its recognition by banks, according to the statistics, was 22 months. This is despite harsher warnings from both the RBI and the government, particularly in the aftermath of the Nirav Modi debacle. To overcome this issue, we demonstrated how to create a predictive loan model that identifies problematic candidates who are considerably more likely to pay the money back. In step-by-step methods, we illustrated how to handle raw data, remove unneeded portions, choose appropriate features, gather exploratory statistics, and finally how to construct a model. In this work, we created supervised learning models such as decision tree (DT), random forest (RF), and k-nearest neighbor (KNN). According to the classification report, the models with the highest accuracy score, f-score, precision, and recall are considered the best among all models. However, in this work, our primary aim was to reduce the false-positive parameter in the classification models’ confusion matrix to reduce the banks’ non-performing assets (NPA), which is helpful to the banking sector. The data were graphed to help bankers better understand the customer’s behavior. Thus, using the same method, client loyalty may also be anticipated.

KENYAN SACCO SOCIETIES IN LIQUIDITY CRUNCH; EVIDENCE FROM CORONAVIRUS PANDEMIC SHOCK

<p>The coronavirus pandemic shock has attenuated economic activity decline in various sectors of the economy across the globe. Financial service providers such as; banks, insurance firms and Sacco societies among others have been impacted by the pandemic shock as livelihoods of the people have been disrupted by the first spreading virus leading to job losses as well as weak demand and supply forces in the market. The unique model of Sacco societies’ business puts them in an even more complex situation which threatens their going concern concept. The uniqueness of the Sacco societies is that they depend on their members’ cumulated savings deposits and shares for financing rather than external borrowings from other financial institutions to conduct their business. Therefore, the study investigated the impact of the coronavirus pandemic shock on the liquidity of Sacco societies in Kenya and specifically, the study examined the covid-19 pandemic shock causal factors, institutional organizations` gaps, exclusion of Sacco societies by the national government in its mitigation measures against covid-19 pandemic shock as well as determining the response strategic interventions by the Sacco societies to stay afloat. The study found out that the majority of the Sacco societies are facing liquidity challenges more especially community, farmer and private-based Sacco societies. The loan demand has declined with long-term loans significantly impacted on by the pandemic shock. In addition, loan default has increased and non-remittance of Sacco deductions as well as withdrawal notices have also spiked up in some Sacco societies. The study discovered that the government has categorically excluded Sacco societies in its mitigation strategies to protect businesses across different sectors and in particular exclusion of Sacco societies from the credit guarantee scheme and non-classification of Sacco societies as essential service providers were cited as some of the evidence towards the exclusion concern. Besides, the Tax Laws (Amendment) Act, 2020 and Finance Bill, 2020 had very little if any touching on the plight of the Sacco societies in the wake of covid-19 pandemic shock. The response strategies instituted by Sacco societies are restructuring of loans, loan rationing, developing of new Sacco packages and recapitalization of returns as well as restricted external borrowings. An exploratory study covering four months from March to June 2020 was conducted among 120 Sacco societies from 11 counties across the country with interview schedules guiding the collection of primary data via telephone calls to the Chief Executive Officers of the targeted Sacco societies. Secondary data was obtained through document analysis of the financial reports of those societies. Judgmental techniques were employed in picking on the Sacco societies which constituted a sample size of 120 Sacco societies although only 75 responded. Data was analysed descriptively with the study findings being presented by the use of tables, graphs and pie charts. The paper recommends that the vulnerability of the Sacco societies in the wake of covid-19 pandemic shock requires immediate external interventions such as the credit guarantee schemes to cushion Sacco societies from the risk of loan default and at the same time improve their liquidity position. The perennial non-remittance of Sacco deductions by institutional organizations needs to be addressed urgently by developing new regulations to deter government organizations and private employers from unnecessarily holding members’ savings and other dues. The community, private and farmer-based Sacco societies seemed to be adversely affected by the covid-19 pandemic shock compared to the teacher and government clusters of Sacco societies. This may be due to the differential livelihoods sectoral impact of covid-19 and the weak financial framework of those Sacco societies to withstand any shock hence consolidation of Sacco societies should be seriously discussed to form entities which are strong enough to withstand shocks.</p><p>JEL: G21; G22; G32</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0537/a.php" alt="Hit counter" /></p>

Macroprudential Policy with Liquidity Panics

Abstract We study the optimality of macroprudential policies in an environment where banks provide liquidity to firms. Informational frictions between banks can cause interbank market freezes, prompting firms to accumulate their own liquid assets. Liquidity hoarding by firms in turn reduces the demand for bank loans and bank profitability, makes interbank market freezes even more likely, and may ultimately trigger a self-fulfilling bad equilibrium. Such “liquidity panics” provide an additional rationale for liquidity requirements on banks, which alleviate frictions in the banking sector and, paradoxically, can increase aggregate investment. Instead, policies encouraging bank lending can have the opposite effect.

Institutional investors, the dollar, and U.S. credit conditions

A strong dollar has been associated with lower lending to emerging markets and tighter global financial conditions. This paper documents similar patterns for credit in the U.S. economy: when the U.S. broad dollar index appreciates by 1 percent, U.S. banks’ corporate loan originations fall by 4.5 percent, with banks tightening credit standards and lending to safer borrowers. This negative correlation, which we term the U.S. dollar credit channel, is at least in part driven by institutional investors, who reduce their demand for risky loans on the secondary loan market when the dollar appreciates. As it becomes harder to sell loans to these investors, banks lend less.

THE PREFERENCES FOR HOUSING LOAN DEMAND IN INDONESIA BEFORE AND AFTER THE LOAN-TO-VALUE POLICY CHANGE

The high price of houses causes a person to not be able to buy a home. The way that someone can own a house is by taking advantage of the credit offers provided by the bank. A mcroprudential policy needed that aims to overcome financial system instability, one of which is the policy on easing the loan-to-value (LTV) ratio for housing loans (KPR). The adjustment of LTV ratio aims to increase the potential demand for property, especially housing and household appliances, in line with an increase in the incomes of the lower middle class, and to control risks in the property sector. This study analyzes the impact of savings, loan interest rates, property sector NPLs, and the number of housing loans before and after a LTV policy revision. The data analysis used the Paired t-test, Wilcoxon signed-rank test, and multiple linear regression. The study results showed that there were differences between savings, loan interest rates, property sector NPLs, and the number of mortgages before and after a LTV policy change. Savings and NPLs in the property sector have no impact on the demand for housing loans while loan interest rates and LTV policy significantly affect the demand for housing loans.

Online Loan Default Prediction Model Based on Deep Learning Neural Network.

With the rapid development of Internet loans and the demand for Internet loans, Internet-based loan default prediction is particularly important. P2P online lending is based on Internet technology. With the popularization of personal PCs and mobile terminals, the borrower's financing cost has been reduced to a large extent, and the efficiency of the borrower's capital utilization has also been improved to a considerable level. Making full use of the existing data of the online lending platform, integrating third-party data, and predicting the default behavior of users are the major directions of future development. This paper mainly studies the network loan default prediction model based on DPNN. This paper first analyzes the problems and risks of the P2P online lending platform, then introduces the principle and characteristics of BPNN in detail, and determines the credit risk rating process for online lending based on BPNN. With the help of data analysis and processing software, after cleaning and variable selection of credit customer data provided by lending clubs, a set of corresponding online lending default risk assessment models are established through BPNN. This paper simulates the network loan default assessment model of the BPNN model and compares it with the support vector machine and regression model. The experimental results show that the highest accuracy rate of the BPNN model is 98.01% and the highest recall rate is 99.82%, which is better than the other two models; the AUC value of BPNN is 0.79, which is significantly higher than that of support vector machine and regression model. The above results show that the online loan default prediction model based on DPNN has high application value in practice. Predicting the probability of customer default risk in advance will help reduce the risk of P2P companies and lenders, improve the competitiveness of P2P lending institutions, and promote the development of domestic P2P platforms to be more stable.

Policy Uncertainty and Bank Mortgage Credit

AbstractWe document that banks reduce the supply of mortgage loans when policy uncertainty increases in their headquarter states as measured by the timing of U.S. gubernatorial elections. The reduction is larger for term‐limited elections and close elections. We utilize high‐frequency, geographically granular loan‐level data to address an identification problem arising from changing local loan demand: (i) we estimate a difference‐in‐difference specification with state/time or county/time fixed effects; (ii) banks reduce lending outside their home states as well when their home states hold elections; and (iii) we observe important cross‐sectional differences in the way banks with different characteristics respond to policy uncertainty.

A nexus between farmland rights, and access, demand, and amount of agricultural loan under the socialist system of China

In the modern socialist system of China, the governments separated land ownership rights and land use rights to increase the access and demand for agricultural loans for the improvement of the social welfare of farmers. Therefore, the current study aims to evaluate the impact of farmland rights and land transfer on access, demand, and amount of agricultural loan under the socialist system of China. For this purpose, data from China Household Finance Survey (CHFS) were used to empirically analyze the objectives of the study using various econometric approaches particularly, the Propensity Score Matching (PSM) method and Benchmark regression. Results depicted that farmland rights and land transfer-in positively and significantly impacted access, demand, and amount of loan. However, the land transfer-out reduced the demand for agricultural loan. Moreover, the land transfer-in found a positive mediating impact on access, demand, and amount of agricultural loan. While the negative mediating effect of the land transfer-out was only reflected in the access and demand for agricultural loan. The farm output of the previous year significantly and positively impacted availing the agricultural loan. While the age of farmers significantly and negatively impacted on availing the agricultural loan. The results also confirmed the regional differences regarding the impact of farmland rights and land transfer-out on availing the agricultural loan. Findings of the study suggest public authorities to increase capital input through state-owned farm assets to reduce the funding gaps and promote agro-environmental sustainability.

Research on credit strategy of small, medium, and micro enterprises based on commercial banks

In order to help banks formulate Credit Strategies for small, medium-sized and micro enterprises, this paper analyzes enterprises from two aspects. On the one hand, starting from the enterprise's data and from the enterprise's relevant data, including qualitative indicators such as reputation level, invoice information, credit record and operating income, a model is established to quantitatively analyze the operation status and reputation status of the enterprise. On the other hand, through the analysis of the "maximum lending rate" and "maximum lending rate" of the bank, we can analyze the "maximum lending rate" and "how much lending rate" of the bank. Combining the above variables, we can get the best credit strategy of the bank under the factors of credit risk and income. Firstly, without considering the influence of other data, this paper abstracts five indicators to measure the reputation status and enterprise-scale from the data of small and medium-sized enterprises with credit rating, quantifies the indicators. At the same time, the Euclidean distance is calculated by MATLAB modeling, the correlation between lending enterprises and non-lending enterprises is analyzed, and the high-risk enterprises in lending enterprises are selected. Then, among the remaining enterprises that can issue loans, five different credit strategies are formulated according to the weighting of their credit rating and enterprise scale as the primary basis for the number of bank loans and interest rate. Secondly, the focus is on how to comprehensively quantify the credit status and business problems of small, medium-sized and micro enterprises without credit records. Without considering the influence of other data, this paper mainly analyzes the correlation between the enterprises without credit records and the enterprises mentioned above that do not grant loans. It preliminarily obtains the enterprises that can grant loans. Then, through decision tree modeling, the loan demand of enterprises with different credit levels is estimated as a variable. By fitting the curve between interest rate and customer churn rate, different interest rate ranges under different levels are obtained as the second variable. Finally, through the above two variables, we establish the profit function of bank credit, find the corresponding variables at the maximum value, and formulate relevant credit strategies.

Державні банки в умовах воєнного стану: ключові проблеми та заходи реагування

Introduction. Ukrainian banking system in general and state-owned banks in particular have faced a set of unprecedented threats, risks and challenges associated with the outbreak of open armed aggression against Ukraine. The negative consequences of hostilities were, in particular, the loss of physical access of banks and customers to some branches and payment infrastructure, the growth of defaults and the suspension of lending. Problem Statement. Improving the stability and reliability of the banking system through the intervention of the central bank and creating favorable conditions for business. Purpose. The aim is to analyze the consequences of the armed conflict on the banking sector and substantiate the list of measures to reduce or neutralize this impact. Methods. General and special methods are used: analysis, synthesis, grouping, description, comparison, theoretical generalization and abstract-logical. Results. The actions of the Government and the National Bank in martial law were generally adequate, including measures to support the most vulnerable groups, reduce tax pressure on business, stimulate business activity by providing cheap loans under a number of government programs, comprehensive support for the banking system through the refinancing mechanism, mitigation of some regulatory requirements. The war made significant adjustments to the strategy of reforming state-owned banks. The structure of households\' funds also changed due to the sudden transformation of the pattern of their behavior. State-owned banks prefer to invest in treasury bills and NBU certificates rather than lending to customers. The main reason for the restrained growth of loan portfolios can be considered a decrease in demand for loans from reliable borrowers, and increasing credit risks. Conclusions. Banks with state capital have demonstrated their financial stability, have not experienced significant withdrawals from accounts, but have preferred to lend to large corporations or provide financial support to SMEs only through government programs of concessional loans. It is expedient to develop national loan restructuring programs together with the banking community, it is time to return to the idea of creating a full-fledged development bank in Ukraine on the basis of existing state-owned banks.

FEATURES OF RECEIVING, ACCOUNTING AND AUDIT OF LOANS IN FOREIGN CURRENCY AT THE ENTERPRISE

The article is devoted to the features of receiving, accounting and auditing of a loan in foreign currency at the enterprise. The topicality of the topic is explained by the fact that ensuring the sustainable development of enterprises requires adequate financing at the expense of various components of equity capital, borrowed capital and loan capital, including lending in foreign currency. the share of balances for loans granted in foreign currency was 29% of the loan portfolio of commercial banks in 2021. This indicates the presence of lending in foreign currency and the need for research and scientific substantiation of the mechanism for obtaining, clearing and auditing loans in foreign currency. The object of the study is crediting, accounting and auditing in foreign currency at the enterprise. The purpose of the study is to substantiate the peculiarities of obtaining, accounting and auditing foreign currency loans at the enterprise to ensure complete and reliable information about the financial obligations of the enterprise. The methods of the study are analysis and synthesis, system analysis, statistical analysis to study the dynamics and structure of loans granted to enterprises by commercial banks in foreign currency, the dynamics of average annual interest rates on loans granted to enterprises. Conditional periods based on indicators of balances of funds for loans granted in foreign currency and the share of loans in foreign currency in the credit portfolio of commercial banks are determined. The first conditional period of development of lending in foreign currency lasted until 2008. It is characterized by a high and constantly growing demand for loans in foreign currency; the share of balances on loans granted in foreign currency was 59% in 2008, a decrease in the exchange rate and low interest rates for using loans in foreign currency against the national currency. The second conditional period of development of foreign currency lending began during the financial crisis of 2008. It is characterized by a 5 times decrease in the balances of loans granted in foreign currency due to the introduced institutional restrictions. Features of lending in foreign currency are determined. The sequence of audit stages of crediting in foreign currency at the enterprise has been developed. Conclusions – despite the institutional restrictions on lending in foreign currency introduced after the financial crisis of 2008, business entities form demand for loans in foreign currency from residents and non-residents. Accounting for loans in foreign currency has certain features and complexity compared to accounting for loans in national currency. Correspondence of accounting accounts for loans received in foreign currency according to typical operations: receiving a loan in foreign currency, accrual and payment of interest for using the loan, repayment or extension of the loan in foreign currency, revaluation of the loan in foreign currency and display of exchange rate differences.

IMPACT OF THE COVID-19 PANDEMIC ON THE DEVELOPMENT OF MORTGAGE LENDING IN RUSSIA

Relevance of the research topic. The coronavirus crisis has had a controversial impact on the mortgage market. On the one hand, mortgages during the Covid-19 pandemic have become more profitable due to lower interest rates and a decrease in the size of the down payment. On the other hand, for many citizens who are faced with the loss of work or a decrease in income, mortgages during the coronavirus period have turned into an overwhelming debt. Government support in the field of mortgage lending during the Covid-19 pandemic spurred the growth of mortgage loans, this support helped to meet part of the demand for mortgage transactions for 2021. Also, banks, from a decrease in interest on preferential mortgages on their proposed programs, received low income, in this situation borrowers who took out a mortgage under the state program in the first three or four months won. Mortgage lending is a very risky financing, both for lenders and borrowers, due to a number of interrelated reasons, such as imperfection of Russian legislation, reduced incomes of the population, and the COVID-19 pandemic. The situation with the COVID-19 pandemic has put many residents of Russia in a difficult position, especially those who, due to restrictive measures, faced disruptions in the activities of their organizations, having significantly lost income, but at the same time bears the burden of paying various kinds of loans, including including mortgage lending. Due to the introduction of the program for concessional mortgage lending, the dynamics of the mortgage market has increased, however, a decline in demand for mortgage loans is predicted after the completion of this program, which will lead to a decrease in the cost of housing. This trend will affect the collateral value of real estate purchased with a mortgage. The main problems of mortgages in the Russian Federation now are low incomes of the country as a whole and of each person individually, significant inflation, monopolization of the housing construction market, migration features, insufficient government participation in supporting mortgages and the underdevelopment of the mortgage securities market. The solution of the indicated restrictions will be possible only as a result of the general, progressive development of the country's economy in all its spheres: politics, social, construction, banking and other sectors. The purpose of the study is to assess the impact of the Covid-19 pandemic on the development of mortgage lending in Russia. Methods of comparison, generalization, analysis, tabular method were used in the work.

Managing Bank Loans By Using Neural Networks

This study aims at recognizing the role of neural networks in deciding administrative decisions in banks. To achieve the aims, the study developed a suggested model that depends on artificial neural networks as a stabilization tool to support loans management decisions. The Descent Conjugate Gradient algorithm is adopted to build the suggested model through checking loan demands according to the various banking instructions. The results showed that using such techniques in administrative business was a success through evaluating loan demands and deciding the most appropriate ones, with the possibility of refusing or accepting the agent’s demand, and also the possibility of deciding the loans which were demanded more than the other types.

Development Of Credit Scoring Model For Borrowers Using Machine Learning Techniques

Financial organizations such as banks have experienced an increase in demand for loans from borrowers over the years. These organizations are highly interested in knowing whether a borrower can pay back if granted the loan requested. Granting loans to defaulters can cripple the business, hence, these financial organizations are compelled to evaluate credit worthiness of clients using the vast volume of historical data related to financial position of borrowers. Like other prediction models, credit scoring is a technique used in predicting the probability that a loan applicant, existing borrower, or counterparty will default. Machine learning technique has ability to solve these challenges faced by credit analyst by automating the processing and extraction of knowledge from data. This research focuses on the development of a credit scoring model using Rough Set Theory (RST) and Multi-Layer Perceptron (MLP) Neural Network. RST was used for feature selection while ANN trained with backpropagation was used for classification. This research used two credit scoring datasets; Australian and German credit dataset. Data pre-processing and machine learning were performed using the Anaconda software. This research compares the result obtained from the RST and MLP with Decision Tree, Logistic Regression, Random Forest, Support Vector Machine, Nayes Bayes, K-Nearest Neighbour and ANN using standard evaluation metric to ascertain its performance on the two datasets and conclude the major findings. This research contributes a credit scoring model with improved performance while saving the computational costs.

금융(Finance)과 기술(Technology)의 융합이 이끌어가는 은행산업 변화에 관한 연구

The purpose of this study is to review the major changes led the first(K Bank), second(Kakao Bank) and third(Toss Bank) Korean virtual banks to the banking industry which had been conducting customer-oriented business activities with branch networks. Therefore we would like to contemplate on the convergence of Finance with IT Technology in the banking industry which was accelerated by the outbreak of COVID-19 pandemic around 2021 when Toss bank started business. The digital transformation of traditional banks has been progressed rapidly since the launch of Korean virtual banks. Kakao Bank successfully achieved an IPO after several series of success but failed to meet the government's goal of meet the medium-rate(Subprime) loan demand of the undeserved class. The establishment of Toss Bank was authorized mainly to supply more medium-rate(Subprime) loan in the personal banking area and, in the meantime, Big tech companies such as Naver were advancing into the financial industry. It is obviously seen that the convergence with the IT technology of the traditional banking industry has been accelerated faster than ever and banking industry itself keeps being revolutionized through Digital Transformation and a bold collaboration with Bigtech such as MyData Business.

EUROZONE SOVEREIGN DEBT CRISIS AND ITS IMPACT ON INDIA'S CROSS-BORDER CREDIT MARKET

This study examines the impact of the Eurozone's sovereign debt crisis on cross-border credit to India, addressing: (i) Causes of subsided lending, (ii) India's loan demand, (iii) Influence of Eurozone banks' home countries on credit, and (vi) Identification of the banking system responsible for the falloff. Results indicate that a 1% change in India's sovereign debt to GDP ratio is linked to a $1013m increase in cross-border lending from the Eurozone. A 1% change in the Eurozone's ratio is associated with a $556m increase in cross-border credit to India. A 1% rise in India's real GDP growth rate is associated with a $164m increase in cross-border bank financing to India. The results indicate that global banks propagate financial shocks from their country of origin. The implications suggest that the concentration of cross-border credit in advanced economy financial systems puts emerging economies like India at risk of country- or region-specific financial shocks.

Nonlinear banking duopoly model with capital regulation: The case of Italy

We analyse a nonlinear banking duopoly model with capital regulation and asymmetric costs. We follow the literature on banking and capital regulation focusing on Italian banks. We extend the banking duopoly model with nonlinear costs of Brianzoni and Campisi (2021), by introducing the hyperbolic inverse demand function, following Puu (1991). In this way, we include a further nonlinear component in the model consisting of nonlinear demand of loans. We proceed in two parts. First, we concentrate on the analysis of the local stability of the model. Given the high number of parameters, we support the analytical study with several numerical simulations. In the second part, we focus on the conditions under which small banks are more efficient than large banks. For this purpose, we study the dynamics of loans when different parameters vary simultaneously. Our results confirm the empirical evidence that small banks play a central role in supporting local firms and families more than large banks.