巴塞尔协议 III 银行法规下的小额信贷机构小额信贷定价模型

IF 2.1 Q2 BUSINESS, FINANCE
Patricia Durango-Gutiérrez, Juan Lara-Rubio, Andrés Navarro-Galera, Dionisio Buendía-Carrillo
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引用次数: 0

摘要

研究目的本研究旨在提出一种工具,用于为小额信贷机构(MFIs)的借款人设计小额信贷风险定价策略。设计/方法/途径。考虑到小额信贷借款人的具体特点,我们首先通过违约概率来估算和衡量小额信贷风险,采用逻辑回归等参数技术和基于人工神经网络的非参数技术,寻找预测能力最强的模型。其次,根据《巴塞尔协议 III》基于内部评级的方法(IRB),我们利用对每个借款人的信用风险测量来设计定价模型,根据违约风险设定小额信贷利率。研究结果本文表明,使用人工神经网络可以更准确地调整每个借款人的违约概率。此外,我们的研究结果表明,在小额贷款机构有盈利目标的情况下,信用风险水平较低的客户的小额贷款利率应低于标准固定利率,以实现盈利目标。实际意义。一方面,这一工具使我们能够衡量和评估小额金融机构的信贷风险,最大限度地减少违约损失;另一方面,通过降低利率、资本要求和信贷损失,提高这些机构的竞争力,从而有利于这些机构在财务上的自我维持。社会影响。我们的研究结果有可能通过提供风险调整定价的小额信贷,使小额信贷机构的借贷行为更加公平公正。此外,我们的研究结果还有助于政府制定旨在促进弱势群体融入金融和社会的政策。独创性。小额信贷客户的个人特征,主要是声誉和道德偿付能力,对小额信贷借款人的违约行为至关重要。这些因素应对小额信贷的定价产生影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Microcredit Pricing Model for Microfinance Institutions under Basel III Banking Regulations
Purpose. The purpose of this research is to propose a tool for designing a microcredit risk pricing strategy for borrowers of microfinance institutions (MFIs). Design/methodology/approach. Considering the specific characteristics of microcredit borrowers, we first estimate and measure microcredit risk through the default probability, applying a parametric technique such as logistic regression and a non-parametric technique based on an artificial neural network, looking for the model with the highest predictive power. Secondly, based on the Basel III internal ratings-based (IRB) approach, we use the credit risk measurement for each borrower to design a pricing model that sets microcredit interest rates according to default risk. Findings. The paper demonstrates that the probability of default for each borrower is more accurately adjusted using the artificial neural network. Furthermore, our results suggest that, given a profitability target for the MFI, the microcredit interest rate for clients with a lower level of credit risk should be lower than a standard, fixed rate to achieve the profitability target. Practical implications. This tool allows us, on the one hand, to measure and assess credit risk and minimize default losses in MFIs and, secondly, to promote their competitiveness by reducing interest rates, capital requirements, and credit losses, favoring the financial self-sustainability of these institutions. Social implications. Our findings have the potential to make microfinance institutions fairer and more equitable in their lending practices by providing microcredit with risk-adjusted pricing. Furthermore, our findings can contribute to the design of government policies aimed at promoting the financial and social inclusion of vulnerable people. Originality. The personal characteristics of microcredit clients, mainly reputation and moral solvency, are crucial to the default behavior of microfinance borrowers. These factors should have an impact on the pricing of microcredit.
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来源期刊
CiteScore
3.70
自引率
8.70%
发文量
100
审稿时长
11 weeks
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