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Relevant bibliographies by topics / Credit risk

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Academic literature on the topic 'Credit risk'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 March 2025

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Journal articles on the topic "Credit risk"

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Michalkova, Lucia, and Katarina Frajtova Michalikova. "Credit risk measurement." New Trends and Issues Proceedings on Humanities and Social Sciences 3, no. 4 (March 22, 2017): 168–74. http://dx.doi.org/10.18844/gjhss.v3i4.1562.

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Hatchett, J. P. L., and R. Kühn. "Credit contagion and credit risk." Quantitative Finance 9, no. 4 (June 2009): 373–82. http://dx.doi.org/10.1080/14697680802464162.

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Brown, Christine A., and Sally Wang. "Credit risk." International Review of Financial Analysis 11, no. 2 (January 2002): 229–48. http://dx.doi.org/10.1016/s1057-5219(02)00076-5.

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Stępińska, Joanna. "Credit Borrower Sector of Economy and Credit Risk For Banks." Journal of Finance and Financial Law 4, no. 44 (December 23, 2024): 95–128. https://doi.org/10.18778/2391-6478.4.44.06.

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The purpose of the article. Business entities in different industries operate differently. Therefore, different sectors are characterized, among other things, by different profitability, the need for external financing, and, consequently, a different level of risk of default on credit commitments to banks. Borrowers from specific industries may be, from the perspective of banks' interests, better or worse customers and contribute to strengthening or weakening the quality of their credit portfolios. The purpose of the article is to compare sections of the economy in terms of the credit risk accompanying banks lending to them. The analysis conducted will be used to verify the hypothesis that industries are significantly different in terms of banks' credit risk related to credits they grant. Methodology. The article supplements the literature with a cross-sectional analysis of economic sectors and includes an assessment of the differentiation of each of them in terms of their economic condition and the associated potential risk of banks lending to their respective industries. The first section of the article presents a literature review covering the issue of comparing the risk of operating in different industries, and the second presents the classification of sections of the economy according to the Polish Classification of Activities (PKD) . The third part presents data from the National Bank of Poland NBP on the repayment of credits in various industries. The fourth section of the article presents the characteristics of each section of the economy. The size of the operating of each industry, taking into account the size of its revenues and the number of enterprises, as well as the scale of investment and the demand for credit and loans in each section of the economy are presented. The cost level index, debt-to-equity ratio, the share of credit s and credits of each section in relation to equity are also examined. The relationships between: debt and financial results of sectors, financial costs and credit, and financial costs and net financial results of industries are also characterized in this chapter. In addition, the profitability and liquidity of economic sections were examined. The fourth chapter also analyzed data on bankruptcy and restructuring proceedings opened by section of the economy. Results of the research. The assumptions and results of the analysis of the risk of credit defaults by each section of the economy are presented in the last section of the article. In addition, the results of the analysis carried out in the article are discussed and compared with NBP data on the share of credits in phases 2 and 3 in each industry. The conclusion of the study is that industries are significantly different in terms of credit risk from the perspective of the banks providing them with financing.

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Eko Muliansyah and Nurmala. "Credit risk, operational risk, and liquidity risk on profitability." World Journal of Advanced Research and Reviews 19, no. 1 (July 30, 2023): 744–52. http://dx.doi.org/10.30574/wjarr.2023.19.1.1426.

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Profitability is the ability of the Village Credit Institution to generate profits and is a ratio that can assess how the Village Credit Institution's ability to generate profits. The high profitability of the Village Credit Institution indicates the good performance of the Village Credit Institution. This study aims to determine the effect of credit risk, operational risk, and liquidity risk on profitability. This research was conducted at the Village Credit Institution for the period 2017-2021. The data collection method used is the non-behavioral observation method with multiple linear regression data analysis techniques. The results showed that Credit Risk has a negative and significant effect on Profitability. Operational Risk has a negative and significant effect on Profitability. Liquidity Risk has a positive and significant effect on Profitability. The profitability of the Village Credit Institution can be maximized by applying the precautionary principle, monitoring and supervising the operations of the Village Credit Institution to minimize costs and provide sufficient liquidity and balanced with good lending.

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Tsintsadze, Asie, Lela Oniani, and Tamar Ghoghoberidze. "Determining and predicting correlation of macroeconomic indicators on credit risk caused by overdue credit." Banks and Bank Systems 13, no. 3 (September 19, 2018): 114–19. http://dx.doi.org/10.21511/bbs.13(3).2018.11.

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The banking system guarantees the economic strength of the country. Its sustainability is due to the sustainability of the credit portfolio. Therefore, scientific research on banking risks is always relevant. Basel recommendations and central bank regulations provide risk minimization in case of default of borrower by creating risk reserve, but the high range of macroeconomic factors creates a basis for creating credit risk. The model, which determines the risk factors, may be structurally the same, but the quality of the influence of factors is different in various countries. The influence of macroeconomic factors is particularly evident in developing countries. The impact of economic factors in different countries is high in GDP of these countries. The article focuses on determining the influence of macroeconomic factors on credit risk of systematic banks in Georgia. The coefficients of individual macroeconomic indicators are calculated by using Pearson’s correlation. The credit risk ratio is taken from the bank’s overdue credits and credit portfolio ratio. Based on the correlation coefficients obtained, the expected risk of shock changes is calculated.

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Ndegwa, Michael K., Apurba Shee, Calum G. Turvey, and Liangzhi You. "Uptake of insurance-embedded credit in presence of credit rationing: evidence from a randomized controlled trial in Kenya." Agricultural Finance Review 80, no. 5 (June 22, 2020): 745–66. http://dx.doi.org/10.1108/afr-10-2019-0116.

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PurposeDrought-related climate risk and access to credit are among the major risks to agricultural productivity for smallholder farmers in Kenya. Farmers are usually credit-constrained due to either involuntary quantity rationing or voluntary risk rationing. By exploiting randomized distribution of weather risk-contingent credit (RCC) and traditional credit, the authors estimate the causal effect of bundling weather index insurance to credit on uptake of agricultural credits among rural smallholders in Eastern Kenya. Further, the authors assess farmers' credit rationing, its determinants and effects on credit uptake.Design/methodology/approachThe study design was a randomized controlled trial (RCT) conducted in Machakos County, Kenya. 1,170 sample households were randomly assigned to one of three research groups, namely control, RCC and traditional credit. This paper is based on baseline household survey data and the first phase of loan implementation data.FindingsThe authors find that 48% of the households were price-rationed, 41% were risk-rationed and 11% were quantity-rationed. The average credit uptake rate was 33% with the uptake of bundled credit being significantly higher than that of traditional credit. Risk rationing seems to influence the credit uptake negatively, whereas premium subsidies do not have any significant association with credit uptake. Among the socio-economic variables, training attendance, crop production being the main household head occupation, expenditure on food, maize labour requirement, hired labour, livestock revenue and access to credit are found to influence the credit uptake positively, whereas the expenditure on non-food items is negatively related with credit uptake.Research limitations/implicationsThe study findings provide important insights on the factors of credit demand. Empirical results suggest that risk rationing is pervasive and discourages farmers to take up credit. The study results also imply that credit demand is inelastic although relatively small sample size for RCC premium subsidy groups may be a limiting factor to the authors’ estimation.Originality/valueBy implementing a multi-arm RCT, the authors estimate the factors affecting the uptake of insurance bundled agricultural credits along with eliciting credit rationing among rural smallholders in Eastern Kenya. This paper provides key empirical findings on the uptake of RCC and the effect of credit rationing on uptake of agricultural credits, a field which has been majorly theoretical.

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Arnold, Lutz G., Johannes Reeder, and Stefanie Trepl. "Single-name Credit Risk, Portfolio Risk and Credit Rationing." Economica 81, no. 322 (February 10, 2014): 311–28. http://dx.doi.org/10.1111/ecca.12075.

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Redondo, Helena, and Elisa Aracil. "Climate‐related credit risk: Rethinking the credit risk framework." Global Policy 15, S1 (March 2024): 21–33. http://dx.doi.org/10.1111/1758-5899.13315.

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AbstractClimate change and the challenges associated with the transition to a zero‐carbon economy pose significant financial risks. Climate‐related risks (CRR) indirectly impact banks through their loan portfolios. To examine the integration of CRR into banks' credit risk assessment and monitoring, this article reviews academic and institutional literature using quantitative bibliometric techniques and content analysis of 145 academic documents from policymakers and financial supervisors. A framework emerges that incorporates CRR into credit risk management. We find four thematic areas in the literature: CRR drivers, CRR tools, CRR data and CRR pricing. Overall, uncertainty, non‐linearity, geographic and industrial dependency and non‐reversibility of CRR difficult climate‐related credit risk assessment. Moreover, CRR data present comparability, availability and reliability issues, which Artificial Intelligence can improve. Finally, evidence reveals that current financial prices do not fully reflect CRR. Our findings provide important implications to policymakers for assessing ex‐ante the financial impacts of climate transition regulations, the potential for prudential regulatory action, and the need for supra‐national policies that facilitate access to reliable and comparable climate data.

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BROLL, UDO, B. MICHAEL GILROY, and ELMAR LUKAS. "MANAGING CREDIT RISK WITH CREDIT DERIVATIVES." Annals of Financial Economics 03, no. 01 (June 2007): 0750004. http://dx.doi.org/10.1142/s2010495207500042.

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Credit risk is one of the most important forms of risk faced by national and international banks as financial intermediaries. Managing this kind of risk through selecting and monitoring corporate and sovereign borrowers and through creating a diversified loan portfolio has always been one of the predominant challenges in bank management. The aim of our study is to examine how a risky loan portfolio affects optimal bank behavior in the loan and deposit markets, when derivatives to hedge credit risk are available. In a stochastic continuous-time framework a hedging model is developed where the bank management can use derivatives to hedge credit risk. Optimal loan, deposit and hedging strategies are then studied. It is shown that the magnitude and the direction of hedging are determined by the bank manager's preferences, the corresponding risk premium and the variance of the loan rate and its hedging instrument respectively.

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Dissertations / Theses on the topic "Credit risk"

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Watson, Ed. "Pricing credit derivatives and credit risk." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ54085.pdf.

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TJONG, VALDY WIYASA. "ANALYZING CREDIT RISK." Thesis, The University of Arizona, 2008. http://hdl.handle.net/10150/192246.

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he, xiaofeng. "CREDIT CYCLE, CREDIT RISK AND BUSINESS CONDITIONS." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20010718-110156.

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We first present a Complex Singular Value Decomposition (CSVD)analysis of credit cyle and explore the lead-lag relation betweencredit cycle and business cycle, then propose a GeneralizedLinear Model (GLM) of credit rating transition probabilitiesunder the impact of business conditions.To detect the cyclic trend existence of credit condition in U.S.economy, all credit variables and business variables aretransformed to complex values and the transformed data matrix isapproximated by first order of CSVD analysis. We show that theeconomy, represented by both credit conditions and businessconditions, is changing recurrently but with different frequenciesfor different time periods. Credit variables making the greatestlinear contribution to first Principal Component can be identifiedas credit cycle indicators. The result of leading businessvariables to credit variables in an economy provides the basis topredict credit condition by business cycle indicators.The credit rating system is a publicly available measure of theriskiness of financial securities and a rating transition matrixquantifies the risk, by permitting calculation of the probabilityof downgrade or default. Credit migration is observed to beinfluenced both by business conditions and by an issuer's owncredit status. We assume the rating history for a particularinstitution is Markovian, and histories for differentinstitutions are assumed to be statistically independent, in bothcases the history of market conditions are known. With a simpleGLM, we investigate the significance of business conditions andtheir two major impacts - creditworthinessdeterioration/improvement and credit stability. We propose amodel of transition probability in discrete time and a model ofinstantaneous transition rates in continuous time, and fit themby maximum likelihood. Business conditions are shown to have asignificant effect: higher likelihood for credit qualityimprovement and stability under good business conditions whilehigher likelihood for credit quality deterioration and driftunder severe business conditions. The two business impacts aresignificant and business deterioration/improvement impact isgreater than its stability impact on credit rating transitions.Investment-grade rating transitions are more sensitive to longrate risk while speculative-grade rating transitions are moresensitive to short rate risk. Compared to a discrete model, thecontinuous transition model has much greater over-dispersion butis more practical.

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Den, Braber Ronald Franciscus Johannes. "Credit risk pricing models as applied to credit trading and risk management." Thesis, Imperial College London, 2006. http://hdl.handle.net/10044/1/7980.

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Sewnath, Neville. "Pricing of credit risk and credit risk derivatives : from theory to implementation." Master's thesis, University of Cape Town, 2008. http://hdl.handle.net/11427/5614.

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Frerichs, Hergen. "Evaluating credit risk models /." [S.l.] : [s.n.], 2003. http://aleph.unisg.ch/hsgscan/hm00105641.pdf.

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Bali, Geetanjali. "Studies in Credit Risk." Thesis, University of Reading, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507020.

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Zhou, Ping. "Essays on credit risk." Thesis, London School of Economics and Political Science (University of London), 2014. http://etheses.lse.ac.uk/945/.

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Xie, Hui. "Sovereign credit risk spillover." Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/27743/.

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This thesis examines cross-market correlations between means and variances in sovereign credit markets and captures the presence of any contagion effect by focusing on parallel movements between markets in the wake of the recent crisis. Furthermore, it focuses on the effect of policy interventions on the dynamics of these correlations. First, to look at the correlation between markets, we investigate the interaction between sovereign spreads and creditworthiness. Our results suggest that there are stable long-term cointegration relationships and significant short-term reactions between government CDS spreads to rating and outlook changes, with rating and outlook leading CDS spreads. After confirming the leading role of credit ratings, we further investigate the spillover effect from ratings to CDS spreads across markets and countries. We are concerned with the spillover effect of a change in the sovereign credit rating and outlook of one country on the sovereign CDS spreads of other countries. We find that rating and outlook announcements originating from different countries have a strong spillover effect across countries but not across regions, while countries’ initial credit status has limited effect on such spillover. Moreover, the US market is a strong source of global spillover to all the countries. After controlling for US factors, the international spillover effects are found to be stronger during crisis periods than in tranquil periods. In addition, credit outlook changes have a greater impact on sovereign CDS spread responses than rating change announcements, suggesting that outlook changes carry more new information. Furthermore, we are also concerned with the influences of rescue plans by the European Union (EU) and the International Monetary Fund (IMF) on the interdependence of sovereign credit risk, measured by CDS spreads, in the Eurozone. The study focuses on the interaction between two groups of nations, ‘cores’ (Austria, Belgium, France, Germany and the UK) and ‘PIIGS’ (Portugal, Ireland, Italy, Greece and Spain), before and after these bailouts. We are able to control for the rating and other external influences affecting sovereign CDS spreads. There are three principal findings. (1) Before the EU interventions, the spreads of the rescued countries – Greece, Ireland, Portugal and Spain (PIGS) – had a strong influence on rating changes in Austria, Belgium, France, Germany and the UK (core European countries). (2) After bailout, our results underline increased interdependencies between sovereign credit risk in the EU area, especially between the rescued country and the core countries. This suggests that these bailout plans not only increase the influence of the rescued country on the development of the core nations, but also amplify the sensitivity of PIIGS to changes in the cores. (3) Different countries will vary in their financial stability and their fundamentals will differ, so they will be expected to respond differently to a bailout. Indeed, distinctive interaction behaviours across countries, related to country-specific characteristics (fiscal outlook), is found for each of the financial policy interventions. Second, to look at the correlation between variances, this study investigated correlation between 9 major EMU countries’ CDS markets during the sovereign debt crisis, and hence examined the impacts of policy interventions on these markets, using the DCC-GARCH model. The main purpose was to assess the extent to which the policy interventions influenced the dynamics of correlations in sovereign CDS markets, after controlling for international influence (US VIX), and both domestic and foreign sovereign credit rating and outlook. Our results suggest that correlations are time-varying for all the sample countries. Most of the policy interventions led to a significant increase in the pairwise correlations. Our interpretation is that the “two-way feedback” between the healthy country and the bailed-out country causes the public-to-public risk transfer. The increased debt and deficit partly result from assisting other troubled nations. Through policy interventions, any deterioration in the sovereign creditworthiness of the healthy countries could transmit back to the bailed-out countries. Moreover, the estimation result suggests that policy interventions, rather than VIX and credit rating/outlook, play the most direct and significant role in shaping the structure of dynamic correlation in the EMU markets.

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Chapman, Zaneta Anne. "Risk, Return and Credit." Diss., Temple University Libraries, 2010. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/82992.

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Business Administration
Ph.D.
This dissertation investigates the role of credit in the evaluation of risk and return. The research comprises three essays, which analyze the use of credit from different perspectives. Chapter 1: The first essay proposes a comprehensive theory for the assessment and implementation of "acceptable" underwriting and rating variables. While the use of personal credit was the driving force behind the essay, we extend our theory and models to include all controversial rating classifications. It is shown that a rating classification would be appropriate when the cost to society is relatively small. The use of personal credit in the automobile insurance industry is provided as an application of the proposed models, and other considerations are explored. Chapter 2: For many years, gamblers have developed strategies to reach specific monetary and survival goals. In the second essay, a strategy is introduced in which a speculator engages in bet doubling to increase his chances of walking home a winner. It is shown that with enough credit it is quite possible to become a winner with a high degree of certainty--99.9%, even while facing a losing proposition. However, huge returns require huge risks, and so adopting such a strategy would eventually lead to large losses and negative expected profits. It is also shown that limited liability and a cost of obtaining credit are important factors to consider when analyzing expected gains. Chapter 3: "Hazardously immoral" contracts force external parties to bear significant losses without their consent. Abuses are particularly likely to occur when the threat of system-wide disruption is sufficient to make governments and international agencies bail out the offending organizations in order to limit total damages. The models provided in chapter 2 are presented in the third essay as strategies for externalizing extreme risks, and several results are derived.
Temple University--Theses

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Books on the topic "Credit risk"

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Bol, Georg, Gholamreza Nakhaeizadeh, Svetlozar T. Rachev, Thomas Ridder, and Karl-Heinz Vollmer, eds. Credit Risk. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-642-59365-9.

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Wagner, Niklas. Credit Risk. London: Taylor and Francis, 2008.

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Gourio, François. Credit risk and disaster risk. Cambridge, MA: National Bureau of Economic Research, 2011.

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He, Zhiguo. Rollover risk and credit risk. Cambridge, MA: National Bureau of Economic Research, 2010.

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Ammann, Manuel. Credit Risk Valuation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-06425-2.

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Bielecki, Tomasz R., Damiano Brigo, and Fédéric Patras. Credit Risk Frontiers. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118531839.

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Witzany, Jiří. Credit Risk Management. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49800-3.

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Bolder, David Jamieson. Credit-Risk Modelling. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94688-7.

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Schirm, Antje. Credit Risk Securitisation. Wiesbaden: Deutscher Universitätsverlag, 2005. http://dx.doi.org/10.1007/978-3-322-81875-1.

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Baesens, Bart, Daniel Rösch, and Harald Scheule. Credit Risk Analytics. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119449560.

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Book chapters on the topic "Credit risk"

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Deutsch, Hans-Peter, and Mark W. Beinker. "Credit Risk." In Derivatives and Internal Models, 471–87. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22899-6_20.

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Bingham, Nicholas H., and Rüdiger Kiesel. "Credit Risk." In Risk-Neutral Valuation, 375–408. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3856-3_9.

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Jones, Stephen A. "Credit Risk." In Trade and Receivables Finance, 87–98. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95735-7_6.

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Vassiliou, P.-C. G. "Credit Risk." In Discrete-time Asset Pricing Models in Applied Stochastic Finance, 323–54. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557860.ch9.

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Bilan, Andrada, Hans Degryse, Kuchulain O’Flynn, and Steven Ongena. "Credit Risk." In Banking and Financial Markets, 61–104. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26844-2_4.

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Roncalli, Thierry. "Credit Risk." In Handbook of Financial Risk Management, 125–255. Boca Raton : CRC Press, 2020. | Series: Chapman and Hall/CRC financial mathematics series: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315144597-3.

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Münnix, Michael C. "Credit Risk." In Studies of Credit and Equity Markets with Concepts of Theoretical Physics, 111–43. Wiesbaden: Vieweg+Teubner, 2011. http://dx.doi.org/10.1007/978-3-8348-8328-5_4.

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Keiding, Hans. "Credit Risk." In Economics of Banking, 127–45. London: Macmillan Education UK, 2016. http://dx.doi.org/10.1007/978-1-137-45305-1_7.

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Cernauskas, Deborah. "Credit Risk." In Essentials of Risk Management in Finance, 214–28. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118387016.ch13.

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Benzin, Arne, Stefan Trück, and Svetlozar T. Rachev. "Approaches to Credit Risk in the New Basel Capital Accord." In Credit Risk, 1–33. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-642-59365-9_1.

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Conference papers on the topic "Credit risk"

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Chafale, Jay H., Renuka S. Wadetwar, Dhanashree M. Giriya, Sagarkumar Badhiye, Pradnya Borkar, and Snehal Shinde. "Credit Risk Analysis using Machine Learning." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA), 1–5. IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10774950.

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L, Taranath N., and Geetha A. "Credit Risk Evaluation using Decision Support System." In 2024 Second International Conference on Advances in Information Technology (ICAIT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690699.

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Shaikh, Danish, Aakash Vishwakarma, Kshitij Patil, Siddhant Roy, and Mimi Cherian. "Credit Risk Assessment." In 2023 International Conference on Advanced Computing Technologies and Applications (ICACTA). IEEE, 2023. http://dx.doi.org/10.1109/icacta58201.2023.10393778.

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Tulum, Catalina. "Credit risk management in banks of the Republic of Moldova." In Simpozion stiintific al tinerilor cercetatori, editia 20. Academy of Economic Studies of Moldova, 2023. http://dx.doi.org/10.53486/9789975359030.60.

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Riscul de credit reprezintă riscul actual sau de viitor ce afectează negativ profiturile sau capitalul băncii ca urmare a neîndeplinirii obligațiilor contractuale de către debitor. Acesta este unul dintre principalele riscuri cu care orice bancă comercială se confruntă iar managementul acestuia reprezintă o parte integrată a proceselor decizionale ale băncii. Gestiunea riscului de credit în bănci cuprinde definirea politicii privind managementul riscurilor în conformitate cu strategia băncii, diversificarea portofoliului de credite, stabilirea limitelor privind riscurile întâlnite, respectarea normativelor stabilite de către Banca Națională a Moldovei. Scopul cercetării vizează importanța gestiunii riscului de credit și îmbunătățirea acestuia pentru o mai bună performanță a băncilor. În urma cercetării s-a concluzionat că gestiunea riscului de credit este importantă întrucât reduce pierderile de venituri. Monitorizarea riscului de credit permite băncilor să analizeze într-o mai mare măsură potențiali clienți, astfel reducând posibilul viitor risc.

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Curdova, Iulia. "Improving credit risk management in a commercial bank." In Simpozion stiintific al tinerilor cercetatori, editia 20. Academy of Economic Studies of Moldova, 2023. http://dx.doi.org/10.53486/9789975359030.59.

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The article considers the relevance of the problem of credit risk management, the concept and methods of credit risk management, problems and methods of credit risk management. The report was made in order to analyze the shortcomings and improve the management of credit risk in a commercial bank in the Republic of Moldova. The subject of the study is the system of financial relations associated with the implementation of banking activities and the emergence of credit risks. The object of the study is the bank's credit risk arising in the course of lending activities in a commercial bank. The paper considers the theoretical foundations of credit risk management, conducts a financial analysis of the main indicators of credit operations in the banking sector of the Republic of Moldova, identifies the problems of credit risk management, and outlines ways to solve them. In conclusion, recommendations were developed to improve the management of credit risk of commercial banks by minimizing it.

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Bace, Edward. "ALM AND CREDIT RISK." In 38th International Academic Conference, Prague. International Institute of Social and Economic Sciences, 2018. http://dx.doi.org/10.20472/iac.2018.038.006.

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7

Ryazanova, Yu Yu, and E. B. Solokhina. "Managing banks' credit risk." In Financial Economics: Topical Development Issues : collection of works of the III International Student Scientific Conference. RIC HGUEP, 2020. http://dx.doi.org/10.38161/978-5-7823-0738-7-2020-152-155.

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8

"Credit risk measurement methodologies." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.d6.allen4.

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9

Wu, Yu-ping, and Cheng-zhang Li. "Study on credit sale risk assessing model based on credit sale risk degree." In 2009 International Conference on Management Science and Engineering (ICMSE). IEEE, 2009. http://dx.doi.org/10.1109/icmse.2009.5317518.

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10

Liu, Huiling, and Yihan Li. "Credit Information Sharing, Bank Size and Bank Credit Risk." In IMMS 2021: 2021 4th International Conference on Information Management and Management Science. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3485190.3485227.

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Reports on the topic "Credit risk"

1

Gourio, Francois. Credit Risk and Disaster Risk. Cambridge, MA: National Bureau of Economic Research, May 2011. http://dx.doi.org/10.3386/w17026.

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2

He, Zhiguo, and Wei Xiong. Rollover Risk and Credit Risk. Cambridge, MA: National Bureau of Economic Research, January 2010. http://dx.doi.org/10.3386/w15653.

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3

Galaasen, Sigurd, Rustam Jamilov, Ragnar Juelsrud, and Hélène Rey. Granular Credit Risk. Cambridge, MA: National Bureau of Economic Research, October 2020. http://dx.doi.org/10.3386/w27994.

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4

Patil, Gitesh. Credit Credit Risk Management Using Hybrid Methodologies. Ames (Iowa): Iowa State University, January 2020. http://dx.doi.org/10.31274/cc-20240624-448.

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5

Berndt, Antje, Rohan Douglas, Darrell Duffie, and Mark Ferguson. Corporate Credit Risk Premia. Cambridge, MA: National Bureau of Economic Research, January 2018. http://dx.doi.org/10.3386/w24213.

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6

López-Piñeros, Martha Rosalba, Fernando Tenjo-Galarza, and Hector Manuel Zárate-Solano. Credit cycles, credit risk and countercyclical loan provisions. Bogotá, Colombia: Banco de la República, November 2013. http://dx.doi.org/10.32468/be.788.

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Gómez, Camilo, and Daniela Rodríguez-Novoa. Firm Support Measures, Credit Payment Behavior, and Credit Risk. Banco de la República, August 2024. http://dx.doi.org/10.32468/be.1277.

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This paper examines the relationship between three government support measures (debt moratorium, credit guarantee programs, and payroll subsidies) and the firm's payment behavior on loans in Colombia. To do so, we use the COVID-19 pandemic episode as a case study. Using highly granular data at the bank-firm level and a difference-in-difference approach, we find that firms subject to debt reliefs and government guarantee programs experienced a lower probability of default while these policies were in force. Subsequently, once the programs ended, the dynamic of the payment behavior of these firms was similar to that of those untreated. On the contrary, payroll subsidies did not affect firms' payment behavior. Regarding the effect on banks' risk assessment, our results suggest that banks modified debtors' credit scores subject to relief programs since the participation status provided them with new information.

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8

Acharya, Viral, Sergei Davydenko, and Ilya Strebulaev. Cash Holdings and Credit Risk. Cambridge, MA: National Bureau of Economic Research, April 2011. http://dx.doi.org/10.3386/w16995.

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9

Soriano, Alejandro. Oversight Note on Credit Risk Management. Inter-American Development Bank, March 2011. http://dx.doi.org/10.18235/0010447.

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This evaluation examines IDB's management of credit risk of Non-Sovereign Guaranteed Operations. Although the IDB is not subject to the Principles for the Management of Credit Risk issued by the Basel Committee for Banking Supervision, these principles have been used as guidelines for this assessment. It can be concluded that the IDB largely complies with Basel's credit risk management principles. To further develop what is already a solid foundation for its credit risk management system, it is recommended that the IDB adopts a comprehensive Credit Risk Framework that clearly defines its risk appetite for NSG. Such framework should spell out the objectives and procedures supporting the desired NSG loan/guarantee portfolio. The strengthening of the Portfolio Management Function is also recommended to complement the current credit administration and risk management functions being carried out by the originating divisions within SCF and OMJ, PMU and RMG. The IDB should also address the risks posed by the current absence of an integrated information system to support the loan/guarantee granting, administration, risk and portfolio management.

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10

Butaru, Florentin, QingQing Chen, Brian Clark, Sanmay Das, Andrew Lo, and Akhtar Siddique. Risk and Risk Management in the Credit Card Industry. Cambridge, MA: National Bureau of Economic Research, June 2015. http://dx.doi.org/10.3386/w21305.

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