Journal articles on the topic 'Credit risk measure'
To see the other types of publications on this topic, follow the link: Credit risk measure.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Credit risk measure.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Hilscher, Jens, and Mungo Wilson. "Credit Ratings and Credit Risk: Is One Measure Enough?" Management Science 63, no. 10 (September 2017): 3414–37. http://dx.doi.org/10.1287/mnsc.2016.2514.
Full text
2
Alam, Pervaiz, Barry Hettler, and Han Gao. "Accounting downside risk measures and credit spreads." Review of Accounting and Finance 20, no. 1 (July 16, 2021): 103–20. http://dx.doi.org/10.1108/raf-08-2020-0244.
Full textAbstract:
Purpose This study aims to examine the association between predictive accounting downside risk measures and changes in credit spreads. Building upon the earnings downside risk (EDR) measure developed in prior literature, this paper introduces cash flow downside risk (CFDR). Design/methodology/approach This study modifies an existing empirical framework (root lower partial moment) to calculate CFDR and applies it to a sample of firms between 2002 and 2013 for which credit default swap data are available. Findings After validating the measure, this study identifies a positive association between CFDR and changes in credit spreads. This paper further shows the association between CFDR and credit spread changes is stronger than that between EDR and credit spread changes. Financial stability moderates the relationship between CFDR and credit spreads. Originality/value This study proposes a novel measure of accounting downside risk, CFDR and demonstrates a negative association between this measure and future cash flow and a positive association between this measure and future credit spreads.
3
Tunay, K. Batu, Hasan F. Yuceyılmaz, and Ahmet Çilesiz. "An International Comparison on Excessive Credit Expansion, Credit Guarantee Programs and The Risks Arising." Khazar Journal of Humanities and Social Sciences 23, no. 1 (2020): 83–102. http://dx.doi.org/10.5782/2223-2621.2020.23.1.83.
Full textAbstract:
Crediting in the banking sector plays an important role in all developed and developing countries. For this reason, it is monitored continuously by public authorities and measures are taken to control credit supply in economic growth periods. On the other hand, in an economic slowdown, when banks are reluctant to increase their credit portfolio, public credit guarantee programs are put into use to increase the credit supply. In this study, a sample covering 26 advanced and emerging economies was analyzed, and the effects of credit gap, credit guarantees and economic growth on credits and arising credit risks were investigated. The findings show that both credits and non-performing loans, an important measure of credit risk, are affected by credit gap, credit guarantees, and economic growth. On the one hand, public credit guarantees positively affect economic growth. On the other hand, though they are widely used for supporting small and medium-sized enterprises, our findings suggest that such expansive credit policies might negatively affect the riskiness of the credit portfolios and soundness of the banking sector.
4
Byström, Hans, and Oh Kang Kwon. "A simple continuous measure of credit risk." International Review of Financial Analysis 16, no. 5 (January 2007): 508–23. http://dx.doi.org/10.1016/j.irfa.2007.03.002.
Full text
5
Kiesel, Florian, and Jonathan Spohnholtz. "CDS spreads as an independent measure of credit risk." Journal of Risk Finance 18, no. 2 (March 20, 2017): 122–44. http://dx.doi.org/10.1108/jrf-09-2016-0119.
Full textAbstract:
Purpose The creditworthiness of corporates is most visible in credit ratings. This paper aims to present an alternative credit rating measure independently of credit rating agencies. The credit rating score (CRS) is based on the credit default swap (CDS) market trading. Design/methodology/approach A CRS is developed which is a linear function of logarithmized CDS spreads. This new CRS is the first one that is completely independent of the rating agency. The estimated ratings are compared with ratings provided by Fitch Ratings for 310 European and US non-financial corporates. Findings The empirical analysis shows that logarithmized CDS spreads and issuer credit ratings by agencies have a linear relationship. The new CRS provides market participants with an alternative risk assessment, which is solely based on market factors, and does not rely on credit rating analysts. The results indicate that our CRS is able to anticipate agency ratings in advance. Moreover, the analysis shows that the trading volume has only a limited influence in the anticipation of rating changes. Originality/value This study shows a new approach to measure the creditworthiness of firms by analyzing CDS spreads. This is highly relevant for regulation, firm monitoring and investors.
6
Fischer, Matthias, Thorsten Moser, and Marius Pfeuffer. "A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations." Risks 6, no. 4 (December 7, 2018): 142. http://dx.doi.org/10.3390/risks6040142.
Full textAbstract:
In both financial theory and practice, Value-at-risk (VaR) has become the predominant risk measure in the last two decades. Nevertheless, there is a lively and controverse on-going discussion about possible alternatives. Against this background, our first objective is to provide a current overview of related competitors with the focus on credit risk management which includes definition, references, striking properties and classification. The second part is dedicated to the measurement of risk concentrations of credit portfolios. Typically, credit portfolio models are used to calculate the overall risk (measure) of a portfolio. Subsequently, Euler’s allocation scheme is applied to break the portfolio risk down to single counterparties (or different subportfolios) in order to identify risk concentrations. We first carry together the Euler formulae for the risk measures under consideration. In two cases (Median Shortfall and Range-VaR), explicit formulae are presented for the first time. Afterwards, we present a comprehensive study for a benchmark portfolio according to Duellmann and Masschelein (2007) and nine different risk measures in conjunction with the Euler allocation. It is empirically shown that—in principle—all risk measures are capable of identifying both sectoral and single-name concentration. However, both complexity of IT implementation and sensitivity of the risk figures w.r.t. changes of portfolio quality vary across the specific risk measures.
7
Sondakh, Jullie Jeanette, Joy Elly Tulung, and Herman Karamoy. "The effect of third-party funds, credit risk, market risk, and operational risk on profitability in banking." Journal of Governance and Regulation 10, no. 2 (2021): 179–85. http://dx.doi.org/10.22495/jgrv10i2art15.
Full textAbstract:
The study aimed to investigate the effect of third-party funds, credit risk, market risk, and operational risk on profitability in banking, especially on the banks included in BUKU 2 category simultaneously or partially. The sampling technique used in the study was saturated sampling. Therefore, a number of 54 banks was obtained as samples. The data in the study were quantitative data, namely in form of financial statements of banking companies included in BUKU 2 category for the period 2014–2017. The data were obtained from the websites of the concerned banks. The research method used was multiple linear regression analysis. In the study, to measure the third-party funds variable we used third-party fund (TPF) ratio, to measure the credit risk variable we used non-performing loan (NPL) and non-performing financing (NPF) ratio, to measure the market risk variable we used net interest margin (NIM) ratio, to measure the operational risk variable we used BOPO ratio, and to measure the profitability variable we used return on assets (ROA) ratio. The result of the study showed that partially third-party funds and credit risk had no significant effect on profitability, partially market risk had a significant positive effect on profitability, and partially credit risk had a significant negative effect on profitability. While simultaneously, third-party funds, credit risk, market risk, and operational risk had a significant effect on profitability.
8
STEIN, HARVEY J. "FIXING RISK NEUTRAL RISK MEASURES." International Journal of Theoretical and Applied Finance 19, no. 03 (April 21, 2016): 1650021. http://dx.doi.org/10.1142/s0219024916500217.
Full textAbstract:
In line with regulations and common risk management practice, the credit risk of a portfolio is managed via its potential future exposures (PFEs), expected exposures (EEs), and related measures, the expected positive exposure (EPE), effective expected exposure (EEE), and the effective expected positive exposure (EEPE). Notably, firms use these exposures to set economic and regulatory capital levels. Their values have a big impact on the capital that firms need to hold to manage their risks. Due to the growth of credit valuation adjustment (CVA) computations, and the similarity of CVA computations to exposure computations, firms find it expedient to compute these exposures under the risk neutral measure. Here, we show that exposures computed under the risk neutral measure are essentially arbitrary. They depend on the choice of numéraire, and can be manipulated by choosing a different numéraire. The numéraire can even be chosen in such a way as to pass backtests. Even when restricting attention to commonly used numéraires, exposures can vary by a factor of two or more. As such, it is critical that these calculations be carried out under the real world measure, not the risk neutral measure. To help rectify the situation, we show how to exploit measure changes to efficiently compute real world exposures in a risk neutral framework, even when there is no change of measure from the risk neutral measure to the real world measure. We also develop a canonical risk neutral measure that can be used as an alternative approach to risk calculations.
9
Wei, Lu, Chen Han, and Yinhong Yao. "The Bias Analysis of Oil and Gas Companies’ Credit Ratings Based on Textual Risk Disclosures." Energies 15, no. 7 (March 24, 2022): 2390. http://dx.doi.org/10.3390/en15072390.
Full textAbstract:
Credit rating bias would affect the capital funding of oil and gas companies, and thus influence the development of the whole economy. Credit rating bias has been mostly analyzed based on different quantitative data sources, and inconsistent results have been obtained. This study first analyzes credit rating bias from the perspective of qualitative textual risk disclosures. By comparing the external credit rating with the internal risk perception expressed in the textual risk disclosures of Form 10-K filings, we can study the consistency of risk assessment of the company by the company’s management and the third-party rating agency. To be specific, four internal textual risk measures and one external risk measure are constructed to quantify the internal risk perception and external risk assessment on oil and gas companies. Then, Spearman’s rho is applied to measure the direction and magnitude of credit rating bias. In the experiment, based on the 357 samples of 174 U.S. oil and gas companies, ranging from 2009 to 2018, we find that the credit ratings mostly overestimate the internal risks perceived by the company managers, and the bias is becoming larger with the credit ratings downgraded from AAA to D.
10
Uberti, Pierpaolo, and Silvia Figini. "How to measure single-name credit risk concentrations." European Journal of Operational Research 202, no. 1 (April 2010): 232–38. http://dx.doi.org/10.1016/j.ejor.2009.05.001.
Full text
11
Jeong, Wan-Ho, and Chan-Pyo Kook. "Stock Return Volatility and Corporate Credit Risk." Journal of Derivatives and Quantitative Studies 20, no. 1 (February 29, 2012): 1–40. http://dx.doi.org/10.1108/jdqs-01-2012-b0001.
Full textAbstract:
In order to reinforce traditional credit indicators such as credit rate or financial ratio, many financial market data; such as the stock prices or their returns are used to evaluate corporate credit risk. Even though many structural models, which are using stock returns and their volatilities, are used to measure credit risk, empirical studies to find out how to measure desirable stock return volatility or which interval data is better for measuring the volatility are not enough. So, we tried to find out empirical evidences of following two questions. First, whether stock return volatility could be used as a timely indicator for credit events, such as bankruptcy or credit rate change. Second, which measure and which interval data are the best to calculate stock return volatility for credit indicator. We have reached the following empirical conclusions based on recent Korean stock market data. First, stock return volatility could be useful for early warning of credit events, because the volatility showed meaningful increase before the credit event. Second, 90~150 daily stock return data are useful to measure the volatility. Short-term data, less than 90 days are too sensitive to market circumstances and they easily increase without any credit level change. On the contrary, volatilities based on long-term data, more than 150 days are too smooth to use as a timely credit indicator. Third, in aspect of the measure of volatility, realized volatility which assume the averages of short-term stock returns are ‘zero’ is more efficient than traditional standard deviation. Those conclusions are based on recent Korean stock market data, so further robustness test should be followed.
12
Barnett, William A., and Liting Su. "RISK ADJUSTMENT OF THE CREDIT-CARD AUGMENTED DIVISIA MONETARY AGGREGATES." Macroeconomic Dynamics 23, S1 (June 6, 2018): 90–114. http://dx.doi.org/10.1017/s1365100518000160.
Full textAbstract:
While credit cards provide transactions services, as do currency and demand deposits, credit cards have never been included in measures of the money supply. The reason is accounting conventions, which do not permit adding liabilities, such as credit card balances, to assets, such as money. However, economic aggregation theory and index number theory measure service flows and are based on microeconomic theory, not accounting. Barnett et al. derived the aggregation and index number theory needed to measure the joint services of credit cards and money. They derived and applied the theory under the assumption of risk neutrality. But since credit card interest rates are high and volatile, risk aversion may not be negligible. We extend the theory by removing the assumption of risk neutrality to permit risk aversion in the decision of the representative consumer.
13
Liu, Yang, Sanjukta Brahma, and Agyenim Boateng. "Impact of ownership structure and ownership concentration on credit risk of Chinese commercial banks." International Journal of Managerial Finance 16, no. 2 (September 30, 2019): 253–72. http://dx.doi.org/10.1108/ijmf-03-2019-0094.
Full textAbstract:
Purpose The purpose of this paper is to examine the effects of bank ownership structure and ownership concentration on credit risk. Design/methodology/approach Using panel data on a sample of 88 Chinese commercial banks, with 826 observations over a period of 2003–2018, this study has applied system generalised method of moments regression to examine the impact of bank ownership structure and ownership concentration on credit risk. This study has used two measures of credit risk, which are non-performing loan ratio (NPLR) and loan loss provision ratio (LLPR). Findings The results show that ownership type (both government and private ownership) exerts a positive and significant impact on credit risk. Measuring ownership concentration using Herfindahl–Hirchmann Index, the results indicate that concentration of ownership in the hands of government has a negative and significant effect on credit risk, whereas private ownership concentration positively impacts credit risk. Overall, the findings suggest that concentration of ownership in government hands reduces risk; however, private ownership concentration exacerbates credit risks. The results are invariant to both measures of credit risk, before and after the financial crisis. Practical implications The findings provide useful insight to guide policy decisions in Chinese banks’ lending policies and bank ownership. Originality/value Using two ex post measures of credit risk, NPLR and LLPR, and one ownership concentration measure, HHI, this study deepens our understanding on the effectiveness of Chinese banks’ corporate governance reforms on managing credit risks.
14
Stanley Isanzu, Juliana. "The Impact of Credit Risk on the Financial Performance of Chinese Banks." JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING 2, no. 3 (2017): 14–17. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.23.3002.
Full textAbstract:
The study aim was to empirically examine the impact of credit risk on the financial performance of Chinese banks. Secondary data was collected from five largest commercial banks in the country for the period of 7 years from 2008 to 2014. The study used nonperforming loans, capital adequacy ratio, impaired loan reserve, and loan impairment charges as measures of credit risk and for a measure of financial performance return on asset was used. Data analysis was done using a balanced panel data regression model, and the study findings reveal nonperforming loan and Capital adequacy have a significant impact of on financial performance of Chinese commercial banks; therefore, the need to control credit risk is crucial for bank financial performance.
15
Burova, Anna, Henry Penikas, and Svetlana Popova. "Probability of Default Model to Estimate Ex Ante Credit Risk." Russian Journal of Money and Finance 80, no. 3 (September 2021): 49–72. http://dx.doi.org/10.31477/rjmf.202103.49.
Full textAbstract:
A genuine measure of ex ante credit risk links borrower’s financial position with the odds of default. Comprehension of a borrower’s financial position is proxied by the derivatives of its filled financial statements, i.e., financial ratios. We identify statistically significant relationships between shortlisted financial ratios and subsequent default events and develop a probability of default (PD) model that assesses the likelihood of a borrower going into delinquency at a one-year horizon. We compare the PD model constructed against alternative measures of ex ante credit risk that are widely used in related literature on bank risk taking, i.e., credit quality groups (prudential reserve ratios) assigned to creditors by banks and the credit spreads in interest rates. We find that the PD model predicts default events more accurately at a horizon of one year compared to prudential reserve rates. We conclude that the measure of ex ante credit risk developed is feasible for estimating risk-taking behaviour by banks and analysing shifts in portfolio composition.
16
Artzner, Philippe, and Freddy Delbaen. "Credit Risk and Prepayment Option." ASTIN Bulletin 22, no. 1 (May 1992): 81–96. http://dx.doi.org/10.2143/ast.22.1.2005128.
Full textAbstract:
AbstractThe paper examines a type of insurance contract for which secondary markets do exist: default risk insurance is implicit in corporate bonds and other risky debts. It applies risk neutral martingale measure pricing to evaluate the option for a borrower with default risk, to prepay a fixed rate loan. A simple “matchbox” example is presented with a spreadsheet treatment.
17
Gottschalk, Sylvia. "Entropy measure of credit risk in highly correlated markets." Physica A: Statistical Mechanics and its Applications 478 (July 2017): 11–19. http://dx.doi.org/10.1016/j.physa.2017.02.083.
Full text
18
Santana, Patricia Jimbo, Laura Lanzarini, and Aurelio F. Bariviera. "Fuzzy Credit Risk Scoring Rules using FRvarPSO." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, Suppl. 1 (December 2018): 39–57. http://dx.doi.org/10.1142/s0218488518400032.
Full textAbstract:
There is consensus that the best way for reducing insolvency situations in financial institutions is through good risk management, which involves a good client selection process. In the market, there are methodologies for credit scoring, each analyzing a large number of microeconomic and/or macroeconomic variables selected mostly depending on the type of credit to be granted. Since these variables are heterogeneous, the review process carried out by credit analysts takes time. The objective of this article is to propose a solution for this problem by applying fuzzy logic to the creation of classification rules for credit granting. To achieve this, linguistic variables were used to help the analyst interpret the information available from the credit officer. The method proposed here combines the use of fuzzy logic with a neural network and a variable population optimization technique to obtain fuzzy classification rules. It was tested with three databases from financial entities in Ecuador — one credit and savings cooperative and two banks that grant various types of credits. To measure its performance, three benchmarks were used: accuracy, number of classification rules generated, and antecedent length. The results obtained indicate that the hybrid model that is proposed performs better than its previous versions due to the addition of fuzzy logic. At the end of the article, our conclusions are discussed and future research lines are suggested.
19
Feng, Jianfen, Dianfa Chen, and Mei Yu. "Pricing Defaultable Securities under Actual Probability Measure." Journal of Systems Science and Information 2, no. 4 (August 25, 2014): 313–34. http://dx.doi.org/10.1515/jssi-2014-0313.
Full textAbstract:
AbstractIn this paper, a new approach is developed to estimate the value of defaultable securities under the actual probability measure. This model gives the price framework by means of the method of backward stochastic differential equation. Such a method solves some problems in most of existing literatures with respect to pricing the credit risk and relaxes certain market limitations. We provide the price of defaultable securities in discrete time and in continuous time respectively, which is favorable to practice to manage real credit risk for finance institutes.
20
Chodnicka-Jaworska, Patrycja. "ESG as a Measure of Credit Ratings." Risks 9, no. 12 (December 14, 2021): 226. http://dx.doi.org/10.3390/risks9120226.
Full textAbstract:
The aim of this study was to examine the impact of environmental, social, and governance (ESG) measures on credit ratings given to non-financial institutions by the largest credit rating agencies according to economic sector divisions. The hypotheses were as follows: a strong negative impact on non-financial institutions’ credit rating changes will result from ESG risk changes, and the reaction of credit rating changes will vary in different sectors. Panel event models were used to verify these hypotheses. The study used data from the Thomson Reuters Database for the period 2010–2020. The analysis was based on the literature on credit rating determinants and on papers and reports on COVID-19, ESG factors, and their impact on credit rating changes. Linear decomposition was used for the analysis. To verify these hypotheses, long-term issuer credit ratings presented by Moody’s and Fitch for European companies listed on these stock exchanges have been used. In the analyses, financial and non-financial factors were also considered. The results suggested that, within the last year, the methodology presented by credit rating agencies has changed, and ESG factors are one of the basic measures that are used to verify credit rating changes, especially those related to the pandemic.
21
Chen, Rongda, Ze Wang, and Lean Yu. "Importance Sampling for Credit Portfolio Risk with Risk Factors Having t-Copula." International Journal of Information Technology & Decision Making 16, no. 04 (April 17, 2017): 1101–24. http://dx.doi.org/10.1142/s0219622017500201.
Full textAbstract:
This paper proposes an efficient simulation method for calculating credit portfolio risk when risk factors have a heavy-tailed distributions. In modeling heavy tails, its features of return on underlying asset are captured by multivariate [Formula: see text]-Copula. Moreover, we develop a three-step importance sampling (IS) procedure in the [Formula: see text]-copula credit portfolio risk measure model for further variance reduction. Simultaneously, we apply the Levenberg–Marquardt algorithm associated with nonlinear optimization technique to solve the problem that estimates the mean-shift vector of the systematic risk factors after the probability measure change. Numerical results show that those methods developed in the [Formula: see text]-copula model can produce large variance reduction relative to the plain Monte Carlo method, to estimate more accurately tail probability of credit portfolio loss distribution.
22
Lu, Su-Lien. "Measuring credit risk by using a parameterized model under risk-neutral measure." Applied Economics Letters 20, no. 8 (May 2013): 719–23. http://dx.doi.org/10.1080/13504851.2012.734593.
Full text
23
Nashikkar, Amrut, Marti G. Subrahmanyam, and Sriketan Mahanti. "Liquidity and Arbitrage in the Market for Credit Risk." Journal of Financial and Quantitative Analysis 46, no. 3 (February 15, 2011): 627–56. http://dx.doi.org/10.1017/s002210901100007x.
Full textAbstract:
AbstractThe recent credit crisis has highlighted the importance of market liquidity and its interaction with the price of credit risk. We investigate this interaction by relating the liquidity of corporate bonds to the basis between the credit default swap (CDS) spread of the issuer and the par-equivalent bond yield spread. The liquidity of a bond is measured using a recently developed measure called latent liquidity, which is defined as the weighted average turnover of funds holding the bond, where the weights are their fractional holdings of the bond. We find that bonds with higher latent liquidity are more expensive relative to their CDS contracts after controlling for other realized measures of liquidity. Analysis of interaction effects shows that highly illiquid bonds of firms with a greater degree of uncertainty are also expensive, consistent with limits to arbitrage between CDS and bond markets, due to the higher costs of “shorting” illiquid bonds. Additionally, we document the positive effects of liquidity in the CDS market on the CDS-bond basis. We also find that several firm- and bond-level variables related to credit risk affect the basis, indicating that the CDS spread does not fully capture the credit risk of the bond.
24
Siddiq, Dr Abbokar, Ebrahim Al Gamal, and Osamah AL-Maamari. "Credit Risk Minimizing: Analysis study of Islamic and conventional banks in Yemen." Journal of Advanced Research in Economics and Administrative Sciences 3, no. 4 (January 6, 2023): 1–8. http://dx.doi.org/10.47631/jareas.v3i4.553.
Full textAbstract:
Purpose: The study aims to compare the credit risk minimization between Islamic and conventional banks in Yemen. Approach/Methodology/Design: This paper is limited to a homogeneous sample that includes the Islamic and conventional banks' coverage as they represent the most significant part of the Yemeni banking sector. Using a descriptive-analytical method, data has been collected by a questionnaire sent by post to each Islamic and conventional bank separately located in Yemen's capital city. Findings: The study concludes that credit risk is the most critical risk facing banks, and there is a significant difference in credit risk minimizing between Islamic. Originality/value: The result showed that the banks' most critical risks are credit risks, and there is a significant difference in credit risk minimisation between Islamic and conventional banks. Also, conventional banks possessed a credit risk minimizing system better than Islamic banks. Several recommendations identified where the Yemeni banks, whether Islamic or conventional, should use advanced methods to measure and analyze credit risks.
25
Harris, Trevor S., Urooj Khan, and Doron Nissim. "The Expected Rate of Credit Losses on Banks' Loan Portfolios." Accounting Review 93, no. 5 (January 1, 2018): 245–71. http://dx.doi.org/10.2308/accr-52012.
Full textAbstract:
ABSTRACT Estimating expected credit losses on banks' portfolios is difficult. The issue has become of increasing interest to academics and regulators with the FASB and IASB issuing new regulations for loan impairment. We develop a measure of the one-year-ahead expected rate of credit losses (ExpectedRCL) that combines various measures of credit risk disclosed by banks. It uses cross-sectional analyses to obtain coefficients for estimating each period's measure of expected credit losses. ExpectedRCL substantially outperforms net charge-offs in predicting one-year-ahead realized credit losses, and reflects nearly all the credit loss-related information in the charge-offs. ExpectedRCL also contains incremental information about one-year-ahead realized credit losses relative to the allowance and provision for loan losses and the fair value of loans. It is a better predictor of the provision for loan losses than analyst provision forecasts, and is incrementally useful beyond other credit risk metrics in predicting bank failure up to one year ahead.
26
Misankova, Maria, and Erika Spuchlakova. "Application of conditional value at risk for credit risk optimization." New Trends and Issues Proceedings on Humanities and Social Sciences 3, no. 4 (March 22, 2017): 146–52. http://dx.doi.org/10.18844/prosoc.v3i4.1540.
Full textAbstract:
The article is dedicated to the optimization of credit risk through the application of Conditional Value at Risk (CVaR). CVaR is a risk measure, the expected loss exceeding Value-at-Risk and is also known as Mean Excess, Mean Shortfall, or Tail VaR. The link between credit risk and the current financial crisis accentuates the importance of measuring and predicting extreme credit risk. Conditional Value at Risk has become an increasingly popular method for measurement and optimization of extreme market risk. The use of model can regulate all positions in a portfolio of financial instruments in order to minimize CVaR subject to trading and return constraints at the same time. The credit risk distribution is created by Monte Carlo simulations and the optimization problem is solved effectively by linear programming. We apply these CVaR techniques to the optimization of credit risk on portfolio of selected bonds.                  Keywords: value at risk; conditional value at risk; credit risk; portfolio
27
Abdul Razak, Lutfi, Mansor H. Ibrahim, and Adam Ng. "Which Sustainability Dimensions Affect Credit Risk? Evidence from Corporate and Country-Level Measures." Journal of Risk and Financial Management 13, no. 12 (December 10, 2020): 316. http://dx.doi.org/10.3390/jrfm13120316.
Full textAbstract:
Amid growing concern over sustainability issues, there is increasing demand to incorporate environmental and social issues into assessments of credit risk, the possibility of loss resulting from a borrower’s failure to meet their financial obligations. In this paper, we sought to identify empirical evidence of a relationship between sustainability measures and credit risk. We contribute to this literature in three main ways: firstly, by using a measure that considers the financial materiality of sustainability issues across different industries; secondly, by using corporate default swap (CDS) spreads as a market-based measure of credit risk; and thirdly, by exploring the context-dependent nature of the relationship. Though the extent differs across industries, our results suggest risk-reducing effects across several corporate sustainability dimensions: climate change; natural resource use; human capital and corporate governance. Furthermore, we found that country sustainability plays a moderating role in the nexus between corporate sustainability and credit risk. Hence, a one-size-fits-all policy may not be suitable in developing the credit-relevant standardization of sustainability factors. Nevertheless, the robustness of corporate governance throughout our findings suggests that corporations should strengthen governance frameworks and procedures prior to embarking on environmental and social objectives to mitigate credit risk.
28
Dang, Van Dan, and Hoang Chung Nguyen. "CREDIT RISK AMID BANKING UNCERTAINTY IN VIETNAM." Buletin Ekonomi Moneter dan Perbankan 25, no. 1 (June 20, 2022): 73–96. http://dx.doi.org/10.21098/bemp.v25i1.1798.
Full textAbstract:
Using a new measure of micro uncertainty based on the cross-sectional dispersion of bank-level shocks, we analyze the impact of banking uncertainty on credit risk in Vietnam during the period 2007–2019. We document that a higher level of banking uncertainty may increase credit risk, and this unfavorable impact is mitigated at larger, better capitalized, and more liquid banks. As compared to private-owned banks, stateowned banks experience higher credit risk during periods of uncertainty. Further analysis supports the “search for yield” hypothesis and helps to better understand why credit risk increases amid uncertainty.
29
I. Dimitras, Augustinos, Stelios Papadakis, and Alexandros Garefalakis. "Evaluation of empirical attributes for credit risk forecasting from numerical data." Investment Management and Financial Innovations 14, no. 1 (March 31, 2017): 9–18. http://dx.doi.org/10.21511/imfi.14(1).2017.01.
Full textAbstract:
In this research, the authors proposed a new method to evaluate borrowers’ credit risk and quality of financial statements information provided. They use qualitative and quantitative criteria to measure the quality and the reliability of its credit customers. Under this statement, the authors evaluate 35 features that are empirically utilized for forecasting the borrowers’ credit behavior of a Greek Bank. These features are initially selected according to universally accepted criteria. A set of historical data was collected and an extensive data analysis is performed by using non parametric models. Our analysis revealed that building simplified model by using only three out of the thirty five initially selected features one can achieve the same or slightly better forecasting accuracy when compared to the one achieved by the model uses all the initial features. Also, experimentally verified claim that universally accepted criteria can’t be globally used to achieve optimal results is discussed.
30
Shen, LiJun, and Yu He. "COVID-19’s influence on the credit risks and enterprise innovation of the Guangxi manufacturing——Based on the measure of KMV model." E3S Web of Conferences 275 (2021): 03071. http://dx.doi.org/10.1051/e3sconf/202127503071.
Full textAbstract:
The paper used the KMV model to manufacturing industry of Guangxi in China to concretely abstract the credit risk and enterprise innovation into a measurable quantitative index, and compare the changes in credit risk before and after COVID-19. This paper selects 17 Listed Companies in Guangxi manufacturing industry as empirical samples, and calculates the expected default rate of different companies by using the traditional and modified KMV models. The larger the index value is, the higher the credit risk is, And then affect the enterprise innovation activities. The results show that the overall credit risk management ability of Guangxi’s manufacturing industry is relatively high, but by the impact of COVID-19, credit risk has increased. If left unguarded, it will have an impact on enterprise innovation.
31
-, Irawati Junaeni. "How Big The Role of Credit Risk, Liquidity Risk and Capital Have an Effect On The Profitability of The 10 Largestt Bank in Indonesia." International Journal of Science, Technology & Management 2, no. 1 (January 27, 2021): 179–89. http://dx.doi.org/10.46729/ijstm.v2i1.146.
Full textAbstract:
The purpose of this research is to analyze how the effect of credit risk, liquidity risk, bank capital, on profitability. The ratio used to measure credit risk using the Non Performing Loan (NPL), liquidity risk using the Loan to Funding Ratio ( LFR) and bank capital using the Capital Adequacy Ratio (CAR). The sample in this study were the 10 largest banks in Indonesia based on total assets. The analysis technique used in this research is panel data regression with fixed effects. The data processing tool used in this study is the Eviews 10 program. The partial test results show that the variables of credit risk and bank capital have an effect on profitabilityas measured by Return on Assets (ROA). Credit risk shows a negative and significant effect on profitability. And bank capital has a positive and significant effect on profitability. Meanwhile, liquidity risk has no significant effect on profitability. Simultaneously, the variables of credit risk, liquidity risk and capital have an effect of 90.17% on profitability. The remaining 9.83% was influenced by other factors not examined in this study
32
Huang, Xin. "Persistence of Bank Credit Default Swap Spreads." Risks 7, no. 3 (August 26, 2019): 90. http://dx.doi.org/10.3390/risks7030090.
Full textAbstract:
Credit default swap (CDS) spreads measure the default risk of the reference entity and have been frequently used in recent empirical papers. To provide a rigorous econometrics foundation for empirical CDS analysis, this paper applies the augmented Dickey–Fuller, Phillips–Perron, Kwiatkowski–Phillips–Schmidt–Shin, and Ng–Perron tests to study the unit root property of CDS spreads, and it uses the Phillips–Ouliaris–Hansen tests to determine whether they are cointegrated. The empirical sample consists of daily CDS spreads of the six large U.S. banks from 2001 to 2018. The main findings are that it is log, not raw, CDS spreads that are unit root processes, and that log CDS spreads are cointegrated. These findings imply that, even though the risks of individual banks may deviate from each other in the short run, there is a long-run relation that ties them together. As these CDS spreads are an important input for financial systemic risk, there are at least two policy implications. First, in monitoring systemic risk, policymakers should focus on long-run trends rather than short-run fluctuations of CDS spreads. Second, in controlling systemic risk, policy measures that reduce the long-run risks of individual banks, such as stress testing and capital buffers, are helpful in mitigating overall systemic risk.
33
Rizky, Bimbi Ardhana, Sudarno Sudarno, and Diah Safitri. "PENGUKURAN RISIKO KREDIT DAN PENGUKURAN KINERJA DARI PORTOFOLIO OBLIGASI." Jurnal Gaussian 7, no. 1 (February 28, 2018): 43–53. http://dx.doi.org/10.14710/j.gauss.v7i1.26634.
Full textAbstract:
Except getting coupon as a profit, there is loss probability in bond investment that is credit risks investment. One way to measure the credit risk of a bond is to use the credit metrics method. It uses the ratings of the bond issuer company and the transition rating issued by the rating company for its calculations. Mean Variance Efficient Portfolio (MVEP) can be used to make an optimal portfolio so that risk can be obtained to a minimum. An assessment of portfolio performance is needed to increase confidence to invest. Sharpe index can measure portfolio performance based on return value of bond. In this case, study has been conduct in two bonds which are Obligasi Berkelanjutan I Bank BTN Tahap II Tahun 2013 and Obligasi Berkelanjutan I PLN Tahap I Tahun 2013 Seri B. The optimum portfolio formed results 67,96% proportion for the first bond and 32,04% for the second bond. For the result, and there is Rp239,4235(billion) of portfolio risk formed. And there is 0,212496for Sharpe index performance assessment portfolio. Keywords: Bond, portfolio, credit risk, credit metrics, Mean Variance Efficient Portfolio, Sharpe index
34
Todorova, Zornitsa. "Network Risk in the European Sovereign CDS Market." Review of Finance and Banking 12, no. 2 (December 31, 2020): 137–54. http://dx.doi.org/10.24818/rfb.20.12.02.03.
Full textAbstract:
This paper applies novel tools from spatial econometrics to measure, quantifyand predict sovereign CDS spreads. Network risk is modelled by making each sovereignísCDS spread a function of the CDS spreads of its ìneighborsî in the Önancial network. Themain Öndings of the paper are: (1) the network model improves forecasting accuracy by 15% to 20%; (2) exogenous Önancial shocks propagate in the network of sovereigns and 40 %to 50% of the total e§ect is due to indirect (network) e§ects. These Öndings suggest analternative explanation to the well-known credit spread puzzle. To rationalize the Öndingsthe paper develops a simple structural network model of sovereign credit risk with Önancialcross-holdings and multiple equilibria.
35
Düllmann, Klaus, and Nancy Masschelein. "A Tractable Model to Measure Sector Concentration Risk in Credit Portfolios." Journal of Financial Services Research 32, no. 1-2 (October 6, 2007): 55–79. http://dx.doi.org/10.1007/s10693-007-0014-3.
Full text
36
Kariya, Takeaki, Yoshiro Yamamura, and Koji Inui. "Empirical Credit Risk Ratings of Individual Corporate Bonds and Derivation of Term Structures of Default Probabilities." Journal of Risk and Financial Management 12, no. 3 (July 23, 2019): 124. http://dx.doi.org/10.3390/jrfm12030124.
Full textAbstract:
Undoubtedly, it is important to have an empirically effective credit risk rating method for decision-making in the financial industry, business, and even government. In our approach, for each corporate bond (CB) and its issuer, we first propose a credit risk rating (Crisk-rating) system with rating intervals for the standardized credit risk price spread (S-CRiPS) measure presented by Kariya et al. (2015), where credit information is based on the CRiPS measure, which is the difference between the CB price and its government bond (GB)-equivalent CB price. Second, for each Crisk-homogeneous class obtained through the Crisk-rating system, a term structure of default probability (TSDP) is derived via the CB-pricing model proposed in Kariya (2013), which transforms the Crisk level of each class into a default probability, showing the default likelihood over a future time horizon, in which 1545 Japanese CB prices, as of August 2010, are analyzed. To carry it out, the cross-sectional model of pricing government bonds with high empirical performance is required to get high-precision CRiPS and S-CRiPS measures. The effectiveness of our GB model and the S-CRiPS measure have been demonstrated with Japanese and United States GB prices in our papers and with an evaluation of the credit risk of the GBs of five countries in the EU and CBs issued by US energy firms in Kariya et al. (2016a, b). Our Crisk-rating system with rating intervals is tested with the distribution of the ratings of the 1545 CBs, a specific agency’s credit rating, and the ratings of groups obtained via a three-stage cluster analysis.
37
Yin, Li-Li, Yi-Wen Qin, Yuan Hou, and Zhao-Jun Ren. "A Convolutional Neural Network-Based Model for Supply Chain Financial Risk Early Warning." Computational Intelligence and Neuroscience 2022 (April 15, 2022): 1–16. http://dx.doi.org/10.1155/2022/7825597.
Full textAbstract:
At present, there are widespread financing difficulties in China's trade circulation industry. Supply chain finance can provide financing for small- and medium-sized enterprises in China’s trade circulation industry, but it will produce financing risks such as credit risks. It is necessary to analyze the causes of the risks in the supply chain finance of the trade circulation industry and measure these risks by establishing a credit risk assessment system. In this article, a supply chain financial risk early warning index system is established, including 4 first-level indicators and 29 third-level indicators. Then, on the basis of the supply chain financial risk early warning index system, combined with the method of convolution neural network, the supply chain financial risk early warning model of trade circulation industry is constructed, and the evaluation index is measured by the method of principal component analysis. Finally, the relevant data of trade circulation enterprises are selected to make an empirical analysis of the model. The conclusion shows that the supply chain financial risk early warning model and risk control measures established in this article have certain reference value for the commercial circulation industry to carry out supply chain finance. It also provides guidance for trade circulation enterprises to deal with supply chain financial risks effectively.
38
Saâda, Moufida Ben, and Yosra Gafsi. "Does disclosure of internal control system of credit risk improve banks’ performance? Evidence from Tunisian listed banks." International Journal of Financial Engineering 06, no. 04 (December 2019): 1950031. http://dx.doi.org/10.1142/s2424786319500312.
Full textAbstract:
This paper proposes a measure of disclosure of internal control of credit risk and explores the extent to which this disclosure improves the performance of Tunisian listed banks. We use a self-constructed disclosure index from content analysis. From regressing panel data applied on a sample of 11 listed Tunisian banks during the period from 2007 to 2017, we find that disclosure of Internal Control System of Credit Risk (DICSCR) improves the performance of banks through the implementation of methods and procedures for controlling credit risk. Moreover, the results show that the interactions between DICSCR and the audit committee, the risk committee, and the internal auditor enhance the performance of the banks. Constructing a measure of disclosure inherent to the internal control system of credit risk allows investors and depositors to make relevant decisions, leads to better understanding the level of risk when controlling the bank by internal auditors and external auditors as well. It provides the Central Bank with a useful tool for evaluating the credit risk of the banks.
39
Li, Jie, and Zhenyu Sheng. "Measuring and Managing Credit Risk for Chinese Microfinance Institutions." International Journal of Economics and Finance 10, no. 7 (June 10, 2018): 56. http://dx.doi.org/10.5539/ijef.v10n7p56.
Full textAbstract:
Chinese microfinance institutions need to measure and manage credit risk in a quantitative way in order to improve competitiveness. To establish a credit scoring model (CSM) with sound predictive power, they should examine various models carefully, identify variables, assign values to variables and reduce variable dimensions in an appropriate way. Microfinance institutions could employ both CSM and loan officer’s subjective appraisals to improve risk management level gradually. The paper sets up a CSM based on the data of a microfinance company running from October 2009 to June 2014 in Jiangsu province. As for establishing the model, the paper uses Linear Discriminant Analysis (LDA) method, selects 16 initial variables, employs direct method to assign variables and adopts all the variables into the model. Ten samples are constructed by randomly selecting records. Based on the samples, the coefficients are determined and the final none-standardized discriminant function is established. It is found that Bank credit, Education, Old client and Rate variables have the greatest impact on the discriminant effect. Compared with the same international models, this model’s classification effect is fine. The paper displays the key technical points to build a credit scoring model based on a practical application, which provides help and references for Chinese microfinance institutions to measure and manage credit risk quantitatively.
40
Bozanic, Zahn, Lin Cheng, and Tzachi Zach. "Soft Information in Loan Agreements." Journal of Accounting, Auditing & Finance 33, no. 1 (February 1, 2017): 40–71. http://dx.doi.org/10.1177/0148558x16689653.
Full textAbstract:
In this study, we seek to understand whether soft information conveyed by contracting language found in private loan agreements is informative regarding borrower risk. We proxy for credit-risk-relevant soft information using Loughran and McDonald’s uncertainty measure. We first examine initial contract terms and find that, incremental to traditional summary measures of credit risk, increased contractual uncertainty is associated with higher initial loan spreads and a greater likelihood of using dynamic and performance-pricing covenants. We then turn to examine realized credit risk over the life of the loan and find that increased uncertainty is associated with a higher likelihood of future loan downgrades and loan amendments. We corroborate our results on the risk relevance of soft information by showing that the bid-ask spreads of loans trading on the secondary loan market are increasing in uncertainty. Overall, the evidence we provide is consistent with embedded linguistic cues in loan agreements publicly revealing the credit risk assessments of privately informed lenders.
41
Imran Hunjra, Ahmed, Tahar Tayachi, Rashid Mehmood, Sidra Malik, and Zoya Malik. "Impact of Credit Risk on Momentum and Contrarian Strategies: Evidence from South Asian Markets." Risks 8, no. 2 (April 14, 2020): 37. http://dx.doi.org/10.3390/risks8020037.
Full textAbstract:
We examine the profitability of the momentum and contrarian strategies in three South Asian markets, i.e., Bangladesh, India, and Pakistan. We also analyze, whether credit risk influences momentum and contrarian return for these markets from 2008 to 2014. We use default risk that relates to non-payments of debts by firms as a measure of credit risk. For that purpose, we use distance to default (DD) by Kealhofer, McQuown, and Vasicek (KMV) model as a proxy of credit risk. We calculate the credit risk and form the momentum and contrarian strategies of the firms based on high, medium, and low risk. We find that in all three markets, the momentum and contrarian returns are significant for medium and high credit risk portfolios and no momentum and contrarian returns for low credit risk portfolios.
42
Huang, Yi. "An Empirical Study on The Default Risk of Kaisa' s Real Estate Industry Before and After COVID-19." BCP Business & Management 32 (November 22, 2022): 330–36. http://dx.doi.org/10.54691/bcpbm.v32i.2910.
Full textAbstract:
In 2019, when COVID-19 outbroke, the real estate economy, an important component of the economy, must be hit to some extent. Affected by the epidemic and financing situation, Kaisa enterprise recently suffered a credit risk. The debt was increasing and the credit ratings were decreasing. The stock of Kaisa was suspended for a long time. This paper used the financial data of Kaisa enterprise between 2019 and 2021 provided by Eastern Wealth and the financial statements of Kaisa enterprise to measure the credit risk of Kaisa enterprise with the KMV model. The empirical results illustrated that the default probability of Kaisa enterprise was relatively high and the default probability increases to some extent after the outbroke of the COVID-19 epidemic. Between 2020 and 2021, Kaisa enterprise was classified as CCC credit rate according to the Standard & Poor's credit rating table. The data and standard confirmed Kaisa enterprise had a high credit risk. To resolve and prevent credit risks, Kaisa enterprise needs to improve the liquidity of funds, carry out reasonable financing and improve the awareness of risk management.
43
RUTKOWSKI, MAREK, and SILVIO TARCA. "REGULATORY CAPITAL MODELING FOR CREDIT RISK." International Journal of Theoretical and Applied Finance 18, no. 05 (July 28, 2015): 1550034. http://dx.doi.org/10.1142/s021902491550034x.
Full textAbstract:
The Basel II internal ratings-based (IRB) approach to capital adequacy for credit risk plays an important role in protecting the banking sector against insolvency. We outline the mathematical foundations of regulatory capital for credit risk, and extend the model specification of the IRB approach to a more general setting than the usual Gaussian case. It rests on the proposition that quantiles of the distribution of conditional expectation of portfolio percentage loss may be substituted for quantiles of the portfolio loss distribution. We present a more compact proof of this proposition under weaker assumptions. Then, constructing a portfolio that is representative of credit exposures of the Australian banking sector, we measure the rate of convergence, in terms of number of obligors, of empirical loss distributions to the asymptotic (infinitely fine-grained) portfolio loss distribution. Moreover, we evaluate the sensitivity of credit risk capital to dependence structure as modeled by asset correlations and elliptical copulas. Access to internal bank data collected by the prudential regulator distinguishes our research from other empirical studies on the IRB approach.
44
Du, Marui, Yue Ma, and Zuoquan Zhang. "A Meta-Path-Based Evaluation Method for Enterprise Credit Risk." Computational Intelligence and Neuroscience 2022 (October 13, 2022): 1–14. http://dx.doi.org/10.1155/2022/1783445.
Full textAbstract:
Nowadays, small and medium-sized enterprises (SMEs) have become an essential part of the national economy. With the increasing number of such enterprises, how to evaluate their credit risk becomes a hot issue. Unlike big enterprises with massive data to analyse, it is hard to find enough primary information of SMEs to assess their financial status, which makes the credit risk evaluation result less accurate. Limited by the lack of primary data, how to infer SMEs’ credit risk from secondary data, such as information about their upstream, downstream, parent, and subsidiary enterprises, attracts big attention from industry and academy. Targeting on accurately evaluating the credit risk of the SME, in this study, we exploit the representative power of the information network on various kinds of SME entities and SME relationships to solve the problem. With that, a heterogeneous information network of SMEs is built to mine enterprise’s secondary information. Furthermore, a novel feature named meta-path feature is proposed to measure the credit risk, which makes us able to evaluate the financial status of SMEs from various perspectives. Experiments show that our proposed meta-path feature is effective to identify SMEs with credit risks.
45
Sun, Han, Hui-zi Ma, and Xiang-rong Wang. "Research on green credit risk measurement based on Pair Copula grouping model--From the perspective of Commercial Banks." E3S Web of Conferences 118 (2019): 03025. http://dx.doi.org/10.1051/e3sconf/201911803025.
Full textAbstract:
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.
46
Cerasi, Vittoria, and Lisa Crosato. "Dimensione e concentrazione dei gruppi bancari italiani nell'ultimo decennio." ECONOMIA E POLITICA INDUSTRIALE, no. 3 (September 2009): 21–39. http://dx.doi.org/10.3280/poli2009-003003.
Full textAbstract:
- The paper analyzes the change in the size distribution of Italian banking groups over the period 1999 to 2007 following a wave of M&As among large banks. Had this process increased the degree of concentration we would have expected greater credit rationing for small firms, given the central role of Italian banks in financing small firms. We measure this change through widely used measures of concentration on branches. First, we observe a steady increase in concentration that can be captured only by looking at the overall size distribution. Other measures do not perceive this change until the year 2007, when the very large banks merged. Second, by focusing on the banking groups that have been active players in M&As we do see a decline in concentration, since smaller players have caught up with the larger ones in terms of rate of size increase. This contrasts with the role of the new entries and the disappearance of banks following mergers, that has increased the dispersion of market shares. The implications are that: i) there is a credit termination risk due to the rise in active players' size, but ii) credit rationing may not occur due to a substitution effect in credit supply from new entries. Keywords: bank market structure; size distribution of banks; measures of concentration; credit rationing of SME; mergers and acquisitions Parole chiave: struttura dell'industria bancaria; distribuzione per dimensione delle banche; misure della concentrazione; razionamento del credito alle PMI; fusioni e acquisizioni Jel Classification: G21 - L11
47
Pandey, Sitaram, and Amitava Samanta. "Impact of Credit Risk on the Profitability of Selected Commercial Banks Listed on the National Stock Exchange." Shodh Sankalp Journal 1, no. 3 (September 1, 2021): 1–15. http://dx.doi.org/10.54051/shodh.2021.1.3.2.
Full textAbstract:
This research is focusing on evaluation of the impact of credit risk on the profitability of selected commercial banks listed on National Stock Exchange. The financial ratios are taken as a proxy to evaluate credit risk and bank’s profitability. Profitability was measured through Return on Equity and Return on Assets whereas credit risk was measured by Pre-Provision Profit to Total Loans and Advances, Loan to Asset Ratio, Capital Adequacy Ratio, Credit to Deposit Ratio and Advances over Loan Funds. Based on the financial information of 2009 to 2017, the study concludes that Credit risk, as calculated from Pre-Provision Profit to Total Loans and Advances, Loan to Asset Ratio, Capital Adequacy Ratio, Credit to Deposit Ratio and Advances over Loan Funds have a non-significant relationship with profitability measured by Return on Assets whereas there is significant relationship exist only between Advances over Loan Funds and profitability measured by Return on Equity. The regression model of ROE shows the model is significant as compared to ROA model. The present study employed Auto Correlation and Durbin-Watson statistics, Unit root test & Multi-Collinearity tests to measure the robustness of time series data. Also the results of the regression analysis show that there exist a negative correlation between credit upon deposit ratio and return on equity. As per the current study, the Indian banks has to keep check on advances upon total funds ratio, as it was found most significant factor impacting the profitability of Indian banks.
48
Di Clemente, Annalisa. "Modeling Portfolio Credit Risk Taking into Account the Default Correlations Using a Copula Approach: Implementation to an Italian Loan Portfolio." Journal of Risk and Financial Management 13, no. 6 (June 17, 2020): 129. http://dx.doi.org/10.3390/jrfm13060129.
Full textAbstract:
This work aims to illustrate an advanced quantitative methodology for measuring the credit risk of a loan portfolio allowing for diversification effects. Also, this methodology can allocate the credit capital coherently to each counterparty in the portfolio. The analytical approach used for estimating the portfolio credit risk is a binomial type based on a Monte Carlo Simulation. This method takes into account the default correlations among the credit counterparties in the portfolio by following a copula approach and utilizing the asset return correlations of the obligors, as estimated by rigorous statistical methods. Moreover, this model considers the recovery rates as stochastic and dependent on each other and on the time until defaults. The methodology utilized for coherently allocating credit capital in the portfolio estimates the marginal contributions of each obligor to the overall risk of the loan portfolio in terms of Expected Shortfall (ES), a risk measure more coherent and conservative than the traditional measure of Value-at-Risk (VaR). Finally, this advanced analytical structure is implemented to a hypothetical, but typical, loan portfolio of an Italian commercial bank operating across the overall national country. The national loan portfolio is composed of 17 sub-portfolios, or geographic clusters of credit exposures to 10,500 non-financial firms (or corporates) belonging to each geo-cluster or sub-portfolio. The outcomes, in terms of correlations, portfolio risk measures and capital allocations obtained from this advanced analytical framework, are compared with the results found by implementing the Internal Rating Based (IRB) approach of Basel II and III. Our chief conclusion is that the IRB model is unable to capture the real credit risk of loan portfolios because it does not take into account the actual dependence structure among the default events, and between the recovery rates and the default events. We underline that the adoption of this regulatory model can produce a dangerous underestimation of the portfolio credit risk, especially when the economic uncertainty and the volatility of the financial markets increase.
49
Kalu, Emenike O., Bashabe Shieler, and Christian U. Amu. "Credit Risk Management and Financial Performance of Microfinance Institutions in Kampala, Uganda." Independent Journal of Management & Production 9, no. 1 (March 2, 2018): 153. http://dx.doi.org/10.14807/ijmp.v9i1.658.
Full textAbstract:
The objective of this study was to evaluate whether relationship exist between credit risk management techniques and financial performance of microfinance institutions in Kampala, Uganda. Specifically, the study examined whether there is a relationship between credit risk identification, credit risk appraisal, credit risk monitoring, credit risk mitigation and financial performance of microfinance institutions in Kampala using sample of 60 members of staff in finance and credit departments of three licensed microfinance institutions in Kampala, Uganda namely Finca Uganda Ltd, Pride Microfinance Ltd, UGAFODE Microfinance Ltd. Primary data was collected using questionnaires and it comprised of closed ended questions. Secondary data was collected from the microfinance institutions (MDI’s) annual reports (2011 - 2015). Frequencies and descriptive statistics were used to analyse the population. Pearson linear correlation coefficient was adopted to examine relationship between credit risk management techniques and financial performance. The findings indicate that credit risk identification and credit risk appraisal has a strong positive relationship on financial performance of MDIs, while credit risk monitoring and credit risk mitigation have moderate significant positive relationship on financial performance of MDIs. The study recommends, among others, that the credit risk appraisal process should identify and analyse all loss exposures, and measure such loss exposures. This should guide in selection of technique or combination of techniques to handle each exposure. The study concludes that MDIs should continually emphasise effective credit risk identification, credit risk appraisal, credit risk monitoring, and credit risk mitigation techniques to enhance maximum financial performance.
50
Wu, Xiaowo, Jiangwei Tu, Boru Liu, Xi Zhou, and Yanxiong Wu. "Credit Risk Evaluation of Forest Farmers under Internet Crowdfunding Mode: The Case of China’s Collective Forest Regions." Sustainability 14, no. 10 (May 11, 2022): 5832. http://dx.doi.org/10.3390/su14105832.
Full textAbstract:
To effectively quantify and control the credit risk of forest farmers under internet crowdfunding mode, the combined weighting of norm grey correlation, the improved analytic hierarchy process and empirical mode decomposition method are proposed to measure the credit risk, and the interval rough number DEMATEL method is used to analyze the credit risk factors of forest farmers. Through the calculation of comprehensive influence degree, it is concluded that the degree of investor information asymmetry, the intensity of supervision, the degree of innovation and cooperation between funders and investors are the main credit risk factors of forest farmers under internet crowdfunding mode, and a credit risk control mechanism is constructed according to the main credit risk factors to effectively improve the risk management and control level of forest farmers.