Relevant bibliographies by topics / Management of credit risk

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Management of credit risk'

Author: Grafiati
Published: 24 April 2022

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Journal articles on the topic "Management of credit risk"

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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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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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Tigges, Christoph. "Buchbesprechung: Die Optimierung der Performance im Credit Management. Verein für Credit Management e.V. (Hrsg.: Schneider-Maessen, Schumann, Skier, Weiß)." RISKNEWS 2, no. 6 (December 2005): 76. http://dx.doi.org/10.1002/risk.200590127.

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Gibson, Michael S. "Credit Derivatives and Risk Management." Finance and Economics Discussion Series 2007, no. 47 (2007): 1–20. http://dx.doi.org/10.17016/feds.2007.47.

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Kealhofer, Stephen. "Portfolio Management of Credit Risk." AIMR Conference Proceedings 2003, no. 5 (February 10, 2003): 19–29. http://dx.doi.org/10.2469/cp.v2003.n5.3318.

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Dissertations / Theses on the topic "Management of credit risk"

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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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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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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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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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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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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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Wendin, Jonathan Erik Purvis. "Bayesian methods in portfolio credit risk management." Zürich : ETH, 2006. http://e-collection.ethbib.ethz.ch/ecol-pool/diss/abstracts/p16481.pdf.

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Malwandla, Musa. "Quantitative models for prudential credit risk management." Doctoral thesis, Faculty of Commerce, 2021. http://hdl.handle.net/11427/33779.

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The thesis investigates the exogenous maturity vintage model (EMV) as a framework for achieving unification in consumer credit risk analysis. We explore how the EMV model can be used in origination modelling, impairment analysis, capital analysis, stress-testing and in the assessment of economic value. The thesis is segmented into five themes. The first theme addresses some of the theoretical challenges of the standard EMV model – namely, the identifiability problem and the forecasting of the components of the model in predictive applications. We extend the model beyond the three time dimensions by introducing a behavioural dimension. This allows the model to produce loan-specific estimates of default risk. By replacing the vintage component with either an application risk or a behavioural risk dimension, the model resolves the identifiability problem inherent in the standard model. We show that the same model can be used interchangeably to produce a point-in-time probability forecast, by fitting a time series regression for the exogenous component, and a through-the-cycle probability forecast, by omitting the exogenous component. We investigate the use of the model for regulatory capital and stress-testing under Basel III, as well as impairment provisioning under IFRS 9. We show that when a Gaussian link function is used the portfolio loss follows a Vašíček distribution. Furthermore, the asset correlation coefficient (as defined under Basel III) is shown to be a function of the level of systemic risk (which is measured by the variance of the exogenous component) and the extent to which the systemic risk can be modelled (which is measured by the coefficient of determination of the regression model for the exogenous component). The second theme addresses the problem of deriving a portfolio loss distribution from a loan-level model for loss. In most models (including the Basel-Vašíček regimes), this is done by assuming that the portfolio is infinitely large – resulting in a loss distribution that ignores diversifiable risk. We thus show that, holding all risk parameters constant, this assumption leads to an understatement of the level of risk within a portfolio – particularly for small portfolios. To overcome this weakness, we derive formulae that can be used to partition the portfolio risk into risk that is diversifiable and risk that is systemic. Using these formulae, we derive a loss distribution that better-represents losses under portfolios of all sizes. The third theme is concerned with two separate issues: (a) the problem of model selection in credit risk and (b) the problem of how to accurately measure probability of insolvency in a credit portfolio. To address the first problem, we use the EMV model to study the theoretical properties of the Gini statistic for default risk in a portfolio of loans and derive a formula that estimates the Gini statistic directly from the model parameters. We then show that the formulae derived to estimate the Gini statistic can be used to study the probability of insolvency. To do this, we first show that when capital requirements are determined to target a specific probability of solvency on a through-the-cycle basis, the point-in-time probability of insolvency can be considerably different from the through-the-cycle probability of insolvency – thus posing a challenge from a risk management perspective. We show that the extent of this challenge will be greater for more cyclical loan portfolios. We then show that the formula derived for the Gini statistic can be used to measure the extent of the point-in-time insolvency risk posed by using a through-the-cycle capital regime. The fourth theme considers the problem of survival modelling with time varying covariates. We propose an extension to the Cox regression model, allowing the inclusion of time-varying macroeconomic variables as covariates. The model is specifically applied to estimate the probability of default in a loan portfolio, where the experience is decomposed the experience into three dimensions: (a) a survival time dimension; (b) a behavioural risk dimension; and (c) calendar time dimension. In this regard, the model can also be viewed as an extension of the EMV model – adding a survival time dimension. A model is built for each dimension: (a) the survival time dimension is modelled by a baseline hazard curve; (b) the behavioural risk dimension is modelled by a behavioural risk index; and (c) the calendar time dimension is modelled by a macroeconomic risk index. The model lends itself to application in modelling probability of default under the IFRS 9 regime, where it can produce estimates of probability of default over variable time horizons, while accounting for time-varying macroeconomic variables. However, the model also has a broader scope of application beyond the domains of credit risk and banking. In the fifth and final theme, we introduce the concept of embedded value to a banking context. In longterm insurance, embedded value relates to the expected economic value (to shareholders) of a book of insurance contracts and is used for appraising insurance companies and measuring management's performance. We derive formulae for estimating the embedded value of a portfolio of loans, which we show to be a function of: (a) the spread between the rate charged to the borrower and the cost of funding; (b) the tenure of the loan; and (c) the level of credit risk inherent in the loan. We also show how economic value can be attributed between profits from maturity transformation and profits from credit and liquidity margin. We derive formulae that can be used to analyse the change in embedded value throughout the life of a loan. By modelling the credit loss component of embedded value, we derive a distribution for the economic value of a book of business. The literary contributions made by the thesis are of practical significance. The thesis offers a way for banks and regulators to accurately estimate the value of the asset correlation coefficient in a manner that controls for portfolio size and intertemporal heterogeneity. This will lead to improved precision in determining capital adequacy – particularly for institutions operating in uncertain environments and those operating small credit portfolios – ultimately enhancing the integrity of the financial system. The thesis also offers tools to help bank management appraise the financial performance of their businesses and measure the value created for shareholders.
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He, Xiao. "User interface suitable for credit risk management." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261153.

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Graphical User Interface, which is known as GUI, is a way for a person to communicate and interact with a system through icons or other visual indicators. A well designed and intuitive user interface is critical to the success of a system since it encourages a natural interaction between a user and a system, thus conveying information more clearly and efficiently to the user.The aim of this study is to design and develop a user interface that is used in a financial technology company in their credit risk assessment process. The current user interface contains a visualization of an individual credit assessment flow together with a lot of data that is generated in the process. Some of the data is not properly visualized, which leads to confusion among end users.In order to optimize the user experience, a user-centered design approach was used combined with a heuristic evaluation. A new user interface was designed and implemented and according to the heuristic evaluation result, the usability was greatly improved. The new interface is able to help the company to visualize their credit risk assessment process in a better way and facilitate credit officers to make credit decisions. The result could also provide insights to other companies or organizations in presenting their data more clearly and effectively.
Grafiskt användargränssnitt, som även kallas GUI, är ett sätt för en person att kommunicera och interagera med ett system genom ikoner eller andra visuella indikatorer. Ett väl utformat och intuitivt användargränssnitt är avgörande för framgången för ett system, eftersom det uppmuntrar till en naturlig interaktion mellan en användare och ett system och därmed förmedlar information tydligare och effektivare till användaren.Syftet med denna studie är att designa och utveckla ett användargränssnitt som används i ett finansiellt teknikföretag i deras kreditriskbedömningsprocess. Det nuvarande användargränssnittet innehåller en visualisering av ett individuellt kreditbedömningsflöde tillsammans med mycket data som genereras i processen. En del av data är inte korrekt visualiserade, vilket leder till förvirring bland slutanvändare.För att optimera användarupplevelsen användes en användarcentrerad designmetod i kombination med en heuristisk utvärdering. Ett nytt användargränssnitt designades och implementerades och enligt det heuristiska utvärderingsresultatet förbättrades användbarheten kraftigt. Det nya gränssnittet kan hjälpa företaget att visualisera sin kreditriskbedömningsprocess på ett bättre sätt och underlätta kreditansvariga att fatta kreditbeslut. Resultatet kan också ge andra företag eller organisationer insikter om att presentera sina uppgifter tydligare och mer effektivt.
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Books on the topic "Management of credit risk"

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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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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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Anolli, Mario, Elena Beccalli, and Tommaso Giordani, eds. Retail Credit Risk Management. London: Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9781137006769.

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Nelson, Richard Warren. Credit card risk management. [United States]: Warren Taylor Publications, 1997.

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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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Book chapters on the topic "Management of credit risk"

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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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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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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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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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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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Conference papers on the topic "Management of credit risk"

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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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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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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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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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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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Gatina, G. F. "Credit risk management of a commercial bank." In Научный диалог: Экономика и менеджмент. ЦНК МОАН, 2018. http://dx.doi.org/10.18411/spc-08-11-2018-01.

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Nakasumi, Mitsuaki. "Credit risk management system on e-Commerce." In the 5th international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/948005.948062.

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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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Reports on the topic "Management of credit risk"

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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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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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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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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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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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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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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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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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