Regulatory Estimates for Defaulted Exposures: A Case Study of Spanish Mortgages

The capital requirements derived from the Basel Accord were issued with the purpose of deploying a transnational regulatory framework. Further regulatory developments on risk measurement is included across several documents published both by the European Banking Authority and the European Central Bank. Among others, the referred additional documentation focused on the models’ estimation and calibration for credit risk measurement purposes, especially the Advanced Internal-Ratings Based models, which may be estimated both for non-defaulted and defaulted assets. A concrete proposal of the referred defaulted exposures models, namely the Expected Loss Best Estimate (ELBE) and the Loss Given Default (LGD) in-default, is presented. The proposed methodology is eventually calibrated on the basis of data from the mortgage’s portfolios of the six largest financial institutions in Spain. The outcome allows for a comparison of the risk profile particularities attached to each of the referred portfolios. Eventually, the economic sense of the results is analyzed.

Research on Credit Risk Measurement of Small and Micro Enterprises Based on the Integrated Algorithm of Improved GSO and ELM

Small and micro enterprises play a very important role in economic growth, technological innovation, employment and social stability etc. Due to the lack of credible financial statements and reliable business records of small and micro enterprises, they are facing financing difficulties, which has become an important factor hindering the development of small and micro enterprises. Therefore, a credit risk measurement model based on the integrated algorithm of improved GSO (Glowworm Swarm Optimization) and ELM (Extreme Learning Machine) is proposed in this paper. First of all, according to the growth and development characteristics of small and micro enterprises in the big data environment, the formation mechanism of credit risk of small and micro enterprises is analyzed from the perspective of granularity scaling, cross-border association and global view driven by big data, and the index system of credit comprehensive measurement is established by summarizing and analyzing the factors that affect the credit evaluation index. Secondly, a new algorithm based on the parallel integration of the good point set adaptive glowworm swarm optimization algorithm and the Extreme learning machine is built. Finally, the integrated algorithm based on improved GSO and ELM is applied to the credit risk measurement modeling of small and micro enterprises, and some sample data of small and micro enterprises in China are collected, and simulation experiments are carried out with the help of MATLAB software tools. The experimental results show that the model is effective, feasible, and accurate. The research results of this paper provide a reference for solving the credit risk measurement problem of small and micro enterprises and also lay a solid foundation for the theoretical research of credit risk management.

ANALISIS MANAJEMEN RISIKO KREDIT SEBGAI ALAT UNTUK MEMINIMALISIR RISKO KREDIT PADA BANK NAGARI CABANG PEMBANTU SIMPANG HARU

This study aims to determine the application of credit risk management and criteria as well as efforts to minimize credit risk in Bank Nagari Simpang Haru Sub-Branch. In implementing credit risk management at Bank Nagari Simpang Haru Sub-Branch, it includes the identification, measurement, monitoring and control of credit risk. Credit risk is the risk of non-performing loans where the debtor must be under special surveillance while the credit measurement must be in accordance with the NPL, Non-perfoming loan (NPL) is very important for credit risk measurement at Bank Nagari Simpang Haru Sub-Branch, because it must be in accordance with the applicable provisions of the Bank Indonesia (BI), by using a non perfoming loan, it will be easy for the Bank to find out the criteria in analyzing credit risk where the Indonesian bank sets a maximum Npl of 5%. Credit collectability is the basis in calculating the level of NPL. Credit Risk Issues that appear at Bank Nagari Simpang Haru Sub-Branch, namely Problem Loans. In this case there are credit risk factors including internal banks, debtors and others. Thus the debtor becomes a factor that often arises and is of special concern.

Measuring the risk of lending to small and medium-sized enterprises in the Republic of Serbia in light of modern banking regulations

The subject matter of this paper is measuring the risk of lending to Small and Medium-sized Enterprises (SMEs) from the point of view of the existing banking regulations. The paper starts from the hypothesis that an increase in the transparency of the credit risk measurement process would enable the timely detection of problems and leave room for the actions necessary for the management of small and mediumsized enterprises, as well as all creditors, and generate an opportunity for SMEs to provide more favorable sources of financing. In the research study, the well-known Altman Z-Score model was used to assess the probability of default and rank a company. The results of the application of the Z-Score model indicate that, to a certain extent, they can detect the companies in which bankruptcy may occur in the two years following the assessment, on the one hand, but they cannot be considered as reliable for the assessment of the probability of the bankruptcy of SMEs in the Republic of Serbia, on the other.

Mashlaha in Financing Risk Measurement in Sharia Financing Institutions

<p><em>The crisis of confidence in the credit rating agency forced Islamic financing institutions to apply risk measurement methods independently and renewed the study of credit risk measurement. Moreover, this research also discusses mashlaha (public interest) in measuring financing risk. This research </em><em>use</em><em>s a mixed method</em><em> approach, </em><em>combining quantitative methods to measure risk by utilizing CreditRisk+</em><em>,</em><em> and qualitative </em><em>methods</em><em> in analyzing mashlaha </em><em>i</em><em>n these measurements. This study revealed that CreditRisk+</em> <em>is able to measure financing risk accurately.</em><em> This study also found that there is mashlaha as part of </em><em>maqashid al-sharia</em><em> in risk measur</em><em>e</em><em>ment</em><em>, namely 1) Tahdzib al-Fard, that mak</em><em>es</em><em> a financial institution capable of independently measuring the risk of its own financing; 2) Iqamah al-Adl, independent measurement will create information justice by comparing measurement results both internally and externally. 3) Mashlaha itself, with internal risk measurement</em><em>,</em><em> will reduce systemic risk. </em><em>The i</em><em>mplications of this study is </em><em>the use of mashlaha in analyzing financing risk provides more stringent prudential in the measurement of financing risk</em><em>.</em></p>

Research on measurement of supply chain finance credit risk based on Internet of Things

This article first expounds the concept of supply chain finance and its credit risk, describes the hierarchical structure of the Internet of Things and its key technologies, and combines the unique functions of the Internet of Things technology and the business process of the inventory pledge financing model to design the supply chain financial model based on the Internet of Things. Then it studies the credit risk assessment under the supply chain financial model based on the Internet of Things, and uses the support vector machine algorithm and Logistic regression method to establish a credit risk measurement model considering the subject rating and debt rating. Finally, an example analysis shows that the credit risk measurement model has a high accuracy rate for determining whether small and medium-sized enterprises in the supply chain financial model based on the Internet of Things are trustworthy. This will facilitate the revision and improvement of the existing credit evaluation system and improve the accuracy of measuring the current financial risk of supply chain. This research adopts the Internet of Things to measure financial credit risk in supply chain and provides a reference for the following researches.

From the Fermi–Dirac distribution to PD curves

Purpose In machine learning applications, and in credit risk modeling in particular, model performance is usually measured by using cumulative accuracy profile (CAP) and receiving operating characteristic curves. The purpose of this paper is to use the statistics of the CAP curve to provide a new method for credit PD curves calibration that are not based on arbitrary choices as the ones that are used in the industry. Design/methodology/approach The author maps CAP curves to a ball–box problem and uses statistical physics techniques to compute the statistics of the CAP curve from which the author derives the shape of PD curves. Findings This approach leads to a new type of shape for PD curves that have not been considered in the literature yet, namely, the Fermi–Dirac function which is a two-parameter function depending on the target default rate of the portfolio and the target accuracy ratio of the scoring model. The author shows that this type of PD curve shape is likely to outperform the logistic PD curve that practitioners often use. Practical implications This paper has some practical implications for practitioners in banks. The author shows that the logistic function which is widely used, in particular in the field of retail banking, should be replaced by the Fermi–Dirac function. This has an impact on pricing, the granting policy and risk management. Social implications Measuring credit risk accurately benefits the bank of course and the customers as well. Indeed, granting is based on a fair evaluation of risk, and pricing is done accordingly. Additionally, it provides better tools to supervisors to assess the risk of the bank and the financial system as a whole through the stress testing exercises. Originality/value The author suggests that practitioners should stop using logistic PD curves and should adopt the Fermi–Dirac function to improve the accuracy of their credit risk measurement.

Research on green credit risk measurement based on Pair Copula grouping model--From the perspective of Commercial Banks

In order to measure the portfolio credit risk of commercial banks in energy saving and environmental protection industry accurately, this paper proposes the value VaRGP of green credit risk and constructs a related model based on Pair Copula grouping model, VaR method (combined with enumeration algorithm).The results show that the credit schemes that commercial banks focus on investing in two areas of industrial emission reduction and environmental restoration is consistent with the conclusion that the two fields have the strongest development momentum.Besides, at different levels of confidence, all of VaRGP have passed the return test, which fully shows that the model is feasible and effective to measure the credit risk in different green fields and to formulate the optimal combination strategy.