A Loan Pricing Model: The Influence of the Lender's Credit Rating

2004 ◽  
Author(s):  
Guy Ford ◽  
Maike Sundmacher
Keyword(s):  
Credit Rating ◽  
Pricing Model ◽  
Loan Pricing

scholarly journals On a New Corporate Bond Pricing Model with Potential Credit Rating Change and Stochastic Interest Rate

2018 ◽  
Vol 11 (4) ◽  
pp. 87 ◽  
Author(s):  
Hong-Ming Yin ◽  
Jin Liang ◽  
Yuan Wu
Keyword(s):  
Interest Rate ◽  
Credit Rating ◽  
Corporate Bond ◽  
Bond Pricing ◽  
Stochastic Interest Rate ◽  
Pricing Model ◽  
New Model ◽  
Stochastic Interest ◽  
The Interest Rate ◽  
Rating Change

In this paper, we consider a new corporate bond-pricing model with credit-rating migration risks and a stochastic interest rate. In the new model, the criterion for rating change is based on a predetermined ratio of the corporation’s total asset and debt. Moreover, the rating changes are allowed to happen a finite number of times during the life-span of the bond. The volatility of a corporate bond price may have a jump when a credit rating for the bond is changed. Moreover, the volatility of the bond is also assumed to depend on the interest rate. This new model improves the previous existing bond models in which the rating change is only allowed to occur once with an interest-dependent volatility or multi-ratings with constant interest rate. By using a Feynman-Kac formula, we obtain a free boundary problem. Global existence and uniqueness are established when the interest rate follows a Vasicek’s stochastic process. Calibration of the model parameters and some numerical calculations are shown.

A pricing model with dynamic credit rating transition matrixes

2021 ◽  
Author(s):  
Yun-Cheng Tsai ◽  
Sheng-Hsuan Lin ◽  
Yuh-Dauh Lyuu
Keyword(s):  
Credit Rating ◽  
Pricing Model

scholarly journals THE IMPACT OF INFORMATION ASYMMETRY, MORAL HAZARD AND THE STRUCTURE OF FUNDING ON CORPORATE U.S. DOLLARS LOAN PRICING: THE EMPIRICAL STUDY IN INDONESIA THE PERIOD 1990-1997

2017 ◽  
Vol 11 (1) ◽  
Author(s):  
Deddy Marciano ◽  
Suad Husnan
Keyword(s):  
Moral Hazard ◽  
Information Asymmetry ◽  
Ordinary Least Squares ◽  
Regression Coefficients ◽  
Special Relationship ◽  
Pricing Model ◽  
Loan Pricing ◽  
Capital Costs ◽  
Increased Risk ◽  
The Impact

This study aims to answer the question: "What factors that influence the price of corporate loans in Indonesia?" And "Are there some differences in loan pricing between several types of creditors?". Furthermore, this research is to develop and test the loan pricing model that was developed in America and Europe to the context or setting in Asia, especially Indonesia. Different conditions and settings of the financial system between America/Europe and Asia, especially Indonesia, causing the loan pricing model that was developed in America/Europe can not be fully implemented for Indonesia. Key issues in this study consisted of: information asymmetry, moral hazard and funding structure. The first issue, information asymmetry consists of the type of creditors, foreign and domestic ownership, public and non-public ownership. The second issue, moral hazard problem consists of variables governmental and non-government ownership, and the special relationship between creditors and debtors. The last issue, creditors’ structure of funding is proxied by the ratio of CD / ML. In addition, this study also adobt the loan pricing models that are developed in America / Europe as control variables. This study also examines the argument of Strahan (1999) whether the loan fees also reflected the condition of the loan as well as loan spreads. The OLS regression (Ordinary Least Squares) with white correction method (White heteroskedasticity correction) for heteroscedasticity problem is conducted to test the model. Various samples and sub samples are prepared to answer various research questions and hypotheses. Testing between regression coefficients are conducted to examine differences in loan pricing between different types of creditors for each variable in the model. The test results generally show that only two new variables suggested by the study, namely: ownership and structure of funding have a significant contribution to the loan pricing model. For variable type of institution consisting of investment banks and commercial banks indicate that generally there is no difference in loan pricing between the two, only in some models of these variables are not significant with signs consistent.Ownership variable show results consistent with the hypothesis and significant effect on loan prices. While the variable special relationship between creditors and debtors have no effect on loan prices, it is due to inter-group loans made by conglomerates. For the case of capital costs of the creditor shows that the variable has a positive effect on lending rates set by creditors. Testing different regression coefficients lead to the conclusion that domestic creditors succeeded in detecting an increased risk of the debtor before the economic crisis of 1997 compared with foreign creditors.

A credit rating model based on a customer number bell-shaped distribution

2018 ◽  
Vol 56 (5) ◽  
pp. 987-1007 ◽  
Author(s):  
Yajing Zhang ◽  
Guotai Chi
Keyword(s):  
Objective Function ◽  
Loss Rate ◽  
Credit Rating ◽  
Credit Ratings ◽  
Absolute Difference ◽  
Loan Pricing ◽  
Content Type ◽  
Rating Model ◽  
Number Of Customers ◽  
Loss Rates

Purpose The purpose of this paper is to split loan customers to different credit ratings to ensure the results that show that customers with lower credit ratings have higher loss rates, and the number of customers that satisfies the bell-shaped distribution. Hence, the number of credit ratings, the distribution of the rated obligors among ratings can achieve a meaningful differentiation of risk, which can avoid the loan pricing confusion. Design/methodology/approach The authors introduce a multi-objective programming to establish the credit rating model. Objective function 1 minimizes the absolute difference between the obligor number proportion and perfect client proportion, following a standard normal distribution. Objective function 2 minimizes the total difference of the deviation between two adjacent credit ratings’ loss rates. This study combines the two objective functions to ensure the obligor number distribution and the monotonicity of the loss rate, and applies genetic algorithm to solve the model. Findings This study’s analysis is based on data from 6,155 enterprises, provided by a Chinese bank and Prosper P2P loan data. The empirical results reveal that the proposed approach can ensure the balance between both criteria and avoid undue concentration of obligors in particular grades. Originality/value The proposed credit model could help building a reasonable credit rating system, which is the prerequisite of loan pricing; thus, inaccurate credit rating can cause incorrect loss rate estimates and loan pricing.

scholarly journals A NEW PRICING APPROACH FOR SME LOANS ISSUED BY COMMERCIAL BANKS BASED ON CREDIT SCORE MAPPING AND ARCHIMEDEAN COPULA SIMULATION

2019 ◽  
Vol 20 (4) ◽  
pp. 618-632
Author(s):  
Chang Liu ◽  
Haoming Shi ◽  
Yujun Cai ◽  
Shu Shen ◽  
Dongtao Lin
Keyword(s):  
Interest Rates ◽  
Credit Rating ◽  
Risk Measures ◽  
Economic Capital ◽  
Archimedean Copula ◽  
Capital Adequacy ◽  
Loan Pricing ◽  
Loan Portfolio ◽  
Credit Score ◽  
Loan Interest Rates

The traditional loans pricing methods are usually based on risk measures of individual loan’s characteristics without considering the correlation between the defaults of different loans and the contribution of individual loans to the entire loan portfolio. In this study, using account-level loans data of 2010-2016 abstracted from 2 databases kindly provided by a Chinese commercial bank, the authors choose Archimedean Copula to fit the default relationship between loans, combined with the loss distribution function constructed to measure the economic capital of the loan portfolio, to propose a loan pricing method that is more suitable for measuring the unique risk characteristic of SMEs loans. Empirical evidence shows that compared with the traditional loan pricing model, this new proposed one, requiring lower loan interest rates from customers with higher credit rating, while higher loan interest rates from customers with lower credit rating, could thus be able to provide higher risk-adjusted returns, higher economic capital adequacy ratios, and ultimately stronger banks’ capabilities to tolerate risk events. Although there might still be some issues and limitations in the study, the method proposed in this study could be of interest not only to the banks’ management, but also to banking regulators as well.

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