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Relevant bibliographies by topics / Credit risk credit risk management

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Academic literature on the topic 'Credit risk credit risk management'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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Journal articles on the topic "Credit risk credit risk management"

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Spuchlakova, Erika, and Maria Misankova. "Risk management of Credit Default Swap." New Trends and Issues Proceedings on Humanities and Social Sciences 3, no. 4 (March 22, 2017): 229–34. http://dx.doi.org/10.18844/gjhss.v3i4.1573.

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Akram, Hassan, and Khalil ur Rahman. "Credit risk management." ISRA International Journal of Islamic Finance 10, no. 2 (December 10, 2018): 185–205. http://dx.doi.org/10.1108/ijif-09-2017-0030.

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PurposeThis study aims to examine and compare the credit risk management (CRM) scenario of Islamic banks (IBs) and conventional banks (CBs) in Pakistan, keeping in view the phenomenal growth of Islamic banking and its future implications.Design/methodology/approachA sample of five CBs and four IBs was chosen out of the whole banking industry for the study. Secondary data obtained from the banks’ annual financial reports for 13 years, starting from 2004 to 2016, were analyzed. Multiple regression, correlation and descriptive analysis were used in the examination of the data.FindingsThe results show that loan quality (LQ) has a positive and significant impact on CRM for both IBs and CBs. Asset quality (AQ), on the other hand, has a negative impact on CRM in the case of IBs, but has a significantly positive relation with CRM in the case of CBs. The impact of 16 ratios measuring LQ and AQ have also been individually checked on CRM, by making use of a regression model using a dummy variable of financial crises for robust comparison among CBs and IBs. The model proved significant, and CRM performance of IBs was observed to be better than that of CBs. Moreover, the mean average value of financial ratios used as a measuring tool for these variables shows that the CRM performance of IBs operating in Pakistan was better than that of CBs over the period of the study.Practical implicationsThe research findings are expected to facilitate bankers, investors, academics and policy makers to build a better understanding of CRM practices as adopted by CBs and IBs. The findings would be useful in formulating policy measures for the progress of the banking industry in Pakistan.Originality/valueThis research is unique in terms of its approach toward analyzing and comparing CRM performance of CBs and IBs. Such work has not been carried out before in the Pakistani banking industry.

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Freeman, Mark C., Paul R. Cox, and Brian Wright. "Credit risk management." Managerial Finance 32, no. 9 (September 2006): 761–73. http://dx.doi.org/10.1108/03074350610681952.

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Gazi, Boran. "Credit Risk Management." Journal of Applied Statistics 38, no. 6 (June 2011): 1314. http://dx.doi.org/10.1080/02664760903335083.

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Kealhofer, Stephen. "Credit Risk and Risk Management." AIMR Conference Proceedings 1999, no. 3 (August 1999): 80–91. http://dx.doi.org/10.2469/cp.v1999.n3.11.

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Boffey, R., and G. N. Robson. "Bank Credit Risk Management." Managerial Finance 21, no. 1 (January 1995): 66–78. http://dx.doi.org/10.1108/eb018497.

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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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Nayan J., Nayan J., and Dr M. Kumaraswamy Dr. M. Kumaraswamy. "Retail Credit Risk Management in Indian Public Sector Banks." Global Journal For Research Analysis 3, no. 8 (June 15, 2012): 31–37. http://dx.doi.org/10.15373/22778160/august2014/10.

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Moloi, Tankiso. "The nature of credit risk information disclosed in the risk and capital reports of the top-5 South African banks." Banks and Bank Systems 11, no. 3 (October 12, 2016): 87–93. http://dx.doi.org/10.21511/bbs.11(3).2016.09.

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This paper used the Credit Risk Disclosure Measurement Tool (CRDMT) constructed on the basis of six main areas, namely, banks own description of credit risk (i.e., as it applies to the banks operations), banks strategy of reducing credit risk exposure (i.e., objectives of credit management), banks approach to credit modelling or the internal rating system, banks approach and the manner in which they assess their exposure to credit risk, banks credit risk mitigation strategies employed (i.e., collateral and other credit enhancements), and banks approach to the valuation of pledged collateral and other credit enhancements to assess the information disclosed on the risk and capital management reports of the top-5 South African banks. Results demonstrated that the top-5 South African banks were fairly in line with the main six credit risk areas that would result in an informative risk and capital management report, as proposed by the CRMDT. It was observed that there were, however, pockets of information that could be improved to enhance these risk and capital management reports, particularly the credit risk information made available to public. These areas included the information relating to banks credit risk mitigation strategies employed and banks strategy of reducing credit risk exposure, as well as the information relating to banks approach to the valuation of pledged collateral and other credit enhancements. These areas were noted for their partial or non-disclosure of information. Keywords: banks, credit risk, Credit Risk Disclosure Measurement Tool (CRDMT), disclosure analysis and risk and capital reports. JEL Classification: G21, G32

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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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Dissertations / Theses on the topic "Credit risk credit risk management"

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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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Zhang, Xuan. "Essays in credit risk management." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/7988/.

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Credit risk management is becoming more and more important in recent years. Credit risk refers to the risk that an obligor fails to make payments on any type of debt at the time of maturity. Credit risk models are statistical tools to infer the future default probabilities and loss distribution of values of a portfolio of debts. This doctoral thesis focus on the application of credit risk management in different areas. To better understand the credit risk management, in the first chapter, we introduce the basic ideas in credit risk management and review the models developed in the last decades. To empirical test the performance of models reviewed in the first chapter, in the second chapter, we compare the reduce-form model with the structural model based on the China’s stock market. It turns out that both models contribute to explaining the default risk of listed firms, however, reduce-form model outperformances the structural model. The empirical results from the second chapter suggests that reduce-form model can better predict the firm’s default risk, but the correlated default risk between firms has not been answered yet. So therefore in the third chapter, we investigate the correlated default risk using copula theory which has been introduced in the first chapter. Based on the insurances firms and other financial firms in the US market, both short-term and long-term default dynamic correlations are found. Another interesting finding from the third chapter is that insurance firms which were considered to be stable actually have higher default risk. This motive us to further explore the determinants of default risk of insurance firms in the fourth chapter and new risk factors (macroeconomic and insurance-specific variables) are found.

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Gu, Jiawen, and 古嘉雯. "On credit risk modeling and credit derivatives pricing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/202367.

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In this thesis, efforts are devoted to the stochastic modeling, measurement and evaluation of credit risks, the development of mathematical and statistical tools to estimate and predict these risks, and methods for solving the significant computational problems arising in this context. The reduced-form intensity based credit risk models are studied. A new type of reduced-form intensity-based model is introduced, which can incorporate the impacts of both observable trigger events and economic environment on corporate defaults. The key idea of the model is to augment a Cox process with trigger events. In addition, this thesis focuses on the relationship between structural firm value model and reduced-form intensity based model. A continuous time structural asset value model for the asset value of two correlated firms with a two-dimensional Brownian motion is studied. With the incomplete information introduced, the information set available to the market participants includes the default time of each firm and the periodic asset value reports. The original structural model is first transformed into a reduced-form model. Then the conditional distribution of the default time as well as the asset value of each name are derived. The existence of the intensity processes of default times is proven and explicit form of intensity processes is given in this thesis. Discrete-time Markovian models in credit crisis are considered. Markovian models are proposed to capture the default correlation in a multi-sector economy. The main idea is to describe the infection (defaults) in various sectors by using an epidemic model. Green’s model, an epidemic model, is applied to characterize the infectious effect in each sector and dependence structures among various sectors are also proposed. The models are then applied to the computation of Crisis Value-at-Risk (CVaR) and Crisis Expected Shortfall (CES). The relationship between correlated defaults of different industrial sectors and business cycles as well as the impacts of business cycles on modeling and predicting correlated defaults is investigated using the Probabilistic Boolean Network (PBN). The idea is to model the credit default process by a PBN and the network structure can be inferred by using Markov chain theory and real-world data. A reduced-form model for economic and recorded default times is proposed and the probability distributions of these two default times are derived. The numerical study on the difference between these two shows that our proposed model can both capture the features and fit the empirical data. A simple and efficient method, based on the ordered default rate, is derived to compute the ordered default time distributions in both the homogeneous case and the two-group heterogeneous case under the interacting intensity default contagion model. Analytical expressions for the ordered default time distributions with recursive formulas for the coefficients are given, which makes the calculation fast and efficient in finding rates of basket CDSs.
published_or_final_version
Mathematics
Doctoral
Doctor of Philosophy

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Pavel, Christoph [Verfasser]. "Credit Portfolio Management An Analysis of Credit Risk Drivers, Models, and Risk Management Tools / Christoph Pavel." München : Verlag Dr. Hut, 2012. http://d-nb.info/1021072990/34.

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Takang, Felix Achou, and Claudine Tenguh Ntui. "Bank performance and credit risk management." Thesis, University of Skövde, School of Technology and Society, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-1318.

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Banking is topic, practice, business or profession almost as old as the very existence of man, but literarily it can be rooted deep back the days of the Renaissance (by the Florentine Bankers). It has sprouted from the very primitive Stone-age banking, through the Victorian-age to the technology-driven Google-age banking, encompassing automatic teller machines (ATMs), credit and debit cards, correspondent and internet banking. Credit risk has always been a vicinity of concern not only to bankers but to all in the business world because the risks of a trading partner not fulfilling his obligations in full on due date can seriously jeopardize the affaires of the other partner.

The axle of this study is to have a clearer picture of how banks manage their credit risk. In this light, the study in its first section gives a background to the study and the second part is a detailed literature review on banking and credit risk management tools and assessment models. The third part of this study is on hypothesis testing and use is made of a simple regression model. This leads us to conclude in the last section that banks with good credit risk management policies have a lower loan default rate and relatively higher interest income.

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Fabík, Peter. "Credit risk management v leasingové společnosti." Master's thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-1580.

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Práce pojednává o řízení rizik v leasingové společnosti. Popisuje proces hodnocení bonity klienta a faktory ovlivňující schvalování obchodních případů. Charakterizuje ratingový a scoringový model v konkrétní leasingové společnosti, hodnotí jejich nedostatky a navrhuje změny na jejich vylepšení. Obsahuje i praktický příklad komplexního hodnocení obchodního případu včetně posouzení bonity klienta prostřednictvím ratingového modelu a nástrojů finanční analýzy.

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Mu, Yuan. "Chinese bank's credit risk assessment." Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/210.

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This thesis studies the Chinese banks’ credit risk assessment using the Post Keynesian approach. We argue that bank loans are the major financial sources in emerging economies and it is uncertainty, an unquantifiable risk, rather than asymmetric information about quantifiable risk, as held by the mainstream approach, which is most important for the risk attached to credit loans, and this uncertainty is particularly important in China. With the universal existence of uncertainty, borrowers and lenders have to make decisions based on convention and experience. With regard to the nature of decision-making, this implies the importance of qualitative methods rather than quantitative methods. The current striking problem in Chinese banking is the large amount of Non-Performing Loans (NPLs) and this research aims to address the NPLs through improving credit risk management. Rather than the previous literature where Western models are introduced into China directly or with minor modification, this work advocates building on China’s conventional domestic methods to deal with uncertainty. We briefly review the background of the Chinese banking history with an evolutionary view and examine Chinese conventions in the development of the credit market. Based on an overview of this history, it is argued that Soft Budget Constraints (SBC) and the underdeveloped risk-assessing mechanism contributed to the accumulation of NPLs. Informed by Western models and experience, we have made several suggestions about rebuilding the Chinese convention of credit risk assessment, based on an analysis of publications and interviews with Chinese bankers. We also suggest some further development of the Asset Management Companies (AMCs) which are used to dispose of the NPLs.

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Erlenmaier, Ulrich. "Risk management in banking credit risk management and bank closure policies /." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963752502.

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Cardella, Laura D. "Credit Risk and Inter-Firm Dependence." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/228116.

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I explore whether inter-firm linkages affect firms' credit risk. After controlling for the endogeneity between a firm's credit risk and its dependence on customers and suppliers, I find that supply-chain relationships affect firms' credit risk. My results indicate firms with exposure to major customers have lower ratings, and the level of firm dependence on major customers is negatively associated with firms' credit ratings. Further, I show when a firm's customers also depend on it, this mitigates the negative effect of dependence on credit risk. Finally, I document a negative association between a customer's reliance on its dependent suppliers and the customer's credit rating. Overall, my results provide insights regarding how inter-firm relationships between corporate customers and suppliers affect credit risk.

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Pryce, Gwilym Benjamin John. "Assessing, perceiving and insuring credit risk." Thesis, University of Glasgow, 1999. http://theses.gla.ac.uk/4960/.

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This thesis is concerned with the assessment, perception and insurance of credit risk. The thesis aims to make contributions both within these areas, and at specific points of interface between them. No attempt is made to develop a single unifying thesis. Rather, a series of partial models are developed, both theoretical and empirical, that develop and connect particular facets of financial economics. The first model demonstrates how movements in market risk produce movements in lender risk-assessment effort. It is demonstrated that deleterious movements in market-wide risk can actually produce a fall in assessment effort. The capricious nature of risk assessment causes changes in the lender's perception of the weights placed on determinants. This has important implications for borrowers' attempts to minimize risk premiums. Time-variability of signal-weights is tested using structural break tests on ordinary least squares and fixed effects panel models. Results suggest a fluid relationship between risk and determinants. Central to empirical investigation is the measurement of perceived risk. A critique of potential measures rejects the use of interest rate spreads - the most commonly used measure - on the basis that they do not take into account the possibility of credit rationing. A model is then constructed to reproduce the standard explanation of credit rationing - Adverse Selection induced Credit Rationing Equilibrium (ASCRE). This model is then extended to include classificatory risk assessment. Assessment is found to reduce the scope for ASCRE, and to cause favourable selection. Credit insurance is then included, and it is found that insurance cover makes risk assessment less of an imperative to lenders, and reduces the utility losses from raising interest rates. The parallel implication is that credit insurance weakens ASCRE, to the extent that full insurance with flat-rate premiums removes the possibility of ASCRE altogether. If the terms of insurance are made contingent on the terms of the loan, a new form of credit rationing emerges: Contingent Insurance induced Credit Rationing Equilibrium (CICRE). CICRE is separate, but not mutually exclusive, to ASCRE. A theoretical model of the demand for loan insurance is developed, and empirically estimated, in the context of the UK mortgage market. Inter alia, the model examines the role of auto-perception of risk determining credit insurance demand. Results reveal the take-up of credit insurance to be relatively insensitive to the borrower's perception ofhis/her own risk.

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Books on the topic "Credit risk credit risk management"

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Colquitt, Joetta. Credit Risk Management. New York: McGraw-Hill, 2007.

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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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Credit risk management. Oxford: Elsevier, 2004.

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

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Siddiqi, Naeem. Credit Risk Scorecards. New York: John Wiley & Sons, Ltd., 2005.

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Managing credit risk. [London?]: Euromoney Books, 2000.

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Abrahams, Clark R. Credit Risk Assessment. New York: John Wiley & Sons, Ltd., 2009.

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John, Barrickman, ed. Strategic credit risk management. Philadelphia, Pa: Robert Morris Associates, 1994.

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Peter, Taylor, and IFS School of Finance, eds. Consumer credit risk management. London, U.K: Global Professional Publishing, 2008.

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Koulafetis, Panayiota. Modern Credit Risk Management. London: Palgrave Macmillan UK, 2017. http://dx.doi.org/10.1057/978-1-137-52407-2.

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Book chapters on the topic "Credit risk credit risk management"

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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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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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Witzany, Jiří. "Credit Derivatives and Counterparty Credit Risk." In Credit Risk Management, 159–239. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49800-3_5.

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Witzany, Jiří. "Credit Risk Management." In Credit Risk Management, 5–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49800-3_2.

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Modina, Michele. "Credit Risk Management." In Credit Rating and Bank-Firm Relationships, 20–47. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137496225_3.

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Nason, Rick. "Credit Risk Management." In Enterprise Risk Management, 261–78. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118267080.ch15.

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Franke, Jürgen, Wolfgang Karl Härdle, and Christian Matthias Hafner. "Credit Risk Management." In Statistics of Financial Markets, 543–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16521-4_22.

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Hener, Alexander. "Credit Risk Management." In Credit Risk Management in the Automotive Industry, 71–80. Wiesbaden: Deutscher Universitätsverlag, 2005. http://dx.doi.org/10.1007/978-3-322-81917-8_5.

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García, Francisco Javier Población. "Credit Risk Management." In Financial Risk Management, 249–63. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41366-2_11.

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Sifri, Jacob E. "Risk Management." In Standby Letters of Credit, 184–94. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/9780230594210_14.

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Conference papers on the topic "Credit risk credit risk management"

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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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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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Yi Zhou. "Credit risk with incomplete information." In 2011 International Conference on Business Management and Electronic Information (BMEI). IEEE, 2011. http://dx.doi.org/10.1109/icbmei.2011.5920457.

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Goda, Shinichi, and Yukio Ohsawa. "Chance Discovery in Credit Risk Management." In Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdmw.2006.40.

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Kvesi�, Ljiljanka, and Gordana Duki�. "Risk Management and Business Credit Scoring." In 34th International Conference on INFORMATION TECHNOLOGY INTERFACES. Zagreb: University Computing Centre - SRCE, 2012. http://dx.doi.org/10.2498/iti.2012.0478.

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Liu, Zhiqiang, and Wenxuan Han. "Litigation Risk and Commercial Credit." In Proceedings of the 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/aebmr.k.191217.170.

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Oliinyk, Andriy, Tetyana Donchenko, Каterina Larionova, and Hennadii Kapinos. "Modeling Credit Risk in Banking." In Proceedings of the 6th International Conference on Strategies, Models and Technologies of Economic Systems Management (SMTESM 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/smtesm-19.2019.47.

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Babaliyev, Eldar. "Development of credit risk management system for non-bank credit organizations." In 2012 IV International Conference "Problems of Cybernetics and Informatics" (PCI). IEEE, 2012. http://dx.doi.org/10.1109/icpci.2012.6486288.

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Жегалова, Елена Валерьевна, and Аник Арамовна Шахбазян. "THE SYSTEM OF RISK MANAGEMENT IN CREDIT INSTITUTIONS." In Социально-экономические и гуманитарные науки: сборник избранных статей по материалам Международной научной конференции (Санкт-Петербург, Февраль 2021). Crossref, 2021. http://dx.doi.org/10.37539/seh295.2021.75.53.003.

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Статья посвящена исследованию особенностей функционирования систем управления рисками в кредитных организациях. В статье на примере ПАО «Сбербанк» рассмотрены особенности управления рыночными рисками кредитных организаций. The article is devoted to the study of the features of the functioning of risk management systems in credit institutions. The article uses the example of PJSC "Sberbank" to consider the features of market risk management of credit institutions.

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Wei, Yani, and Zhangyong Xu. "Research on Credit Risk of Corporate Bond." In 2017 International Conference on Management Science and Management Innovation (MSMI 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/msmi-17.2017.30.

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Reports on the topic "Credit risk credit risk management"

1

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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2

Lozano-Espitia, Ignacio, and Fernando Arias-Rodríguez. The Relationship between Fiscal and Monetary Policies in Colombia: An Empirical Exploration of the Credit Risk Channel. Banco de la República, April 2022. http://dx.doi.org/10.32468/be.1196.

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Abstract:

This paper aims to provide evidence on the relationship between fiscal and monetary policy in Colombia through an empirical exploration of the credit risk channel. Under this approach, fiscal policy plays an important explanatory role in the sovereign risk premium, which, in turn, could aﬀect the exchange rate and inﬂation expectations. The Central Bank reacts to inﬂation expectations using the policy interest rate; consequently, such reaction could be indirectly inﬂuenced by fiscal behavior. Using monthly data from January 2003 to December 2019, we estimate both jointly and independently the reduced-form core equations of a system that describes the credit risk channel in a small open economy. Our findings are in line with the model predictions. Fiscal policy aﬀected the country’s sovereign risk during this period, but only slightly. Hence, there is insufcient evidence to sustain the idea that monetary policy has been signifcantly inﬂuenced by government fiscal management.

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3

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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4

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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5

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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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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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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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

Fleckenstein, Matthias, and Francis Longstaff. The Market Risk Premium for Unsecured Consumer Credit Risk. Cambridge, MA: National Bureau of Economic Research, October 2020. http://dx.doi.org/10.3386/w28029.

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10

Baron, Matthew, and Wei Xiong. Credit Expansion and Neglected Crash Risk. Cambridge, MA: National Bureau of Economic Research, September 2016. http://dx.doi.org/10.3386/w22695.

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