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1

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

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2

Lowe, Philip. Credit risk measurement and procyclicality. Basel, Switzerland: Bank for International Settlements, Monetary and Economic Dept., 2002.

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3

Doumpos, Michalis, Christos Lemonakis, Dimitrios Niklis, and Constantin Zopounidis. Analytical Techniques in the Assessment of Credit Risk. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-99411-6.

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4

Pershad, Rinku. A Bayesian belief network for corporate credit risk assessment. Ottawa: National Library of Canada, 2000.

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5

Somerville, Andrew A. The reliability of banker judgement in LDC credit-risk assessment. London: City University Business School, Centre for Empirical Research in Finance and Accounting, 1994.

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6

Renato, Maino, and Molteni Luca, eds. Developing, validating, and using internal ratings: Methodologies and case studies. Hoboken, NJ: Wiley, 2010.

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7

Dermine, Jean. Deposit insurance, credit risk and capital adequacy: A note. Fontainebleau: INSEAD, 1992.

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8

Avery, Robert B. Consumer credit scoring: Do situational circumstances matter? Basel, Switzerland: Bank for International Settlements, 2004.

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9

Ganguin, Blaise. Fundamentals of corporate credit analysis. New York: McGraw-Hill, 2005.

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10

Worrell, DeLisle. Quantitative assessment of the financial sector: An integrated approach. [Washington D.C.]: International Monetary Fund, Monetary and Financial Systems Dept., 2004.

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11

Pagès, Henri. Can liquidity risk be subsumed in credit risk?: A case study from Brady bond prices. Basel, Switzerland: Bank for International Settlements, Monetary and Economic Dept., 2001.

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12

Mingyuan, Zhang, ed. Credit risk assessment: The new lending system for borrowers, lenders, and investors. Hoboken, NJ: Wiley, 2009.

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13

Bohn, Jeffrey R. Active Credit Portfolio Management in Practice. New York: John Wiley & Sons, Ltd., 2009.

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14

1966-, Stein Roger M., ed. Active credit portfolio management in practice. Hoboken, N.J: Wiley, 2009.

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15

Linda, Allen. A survey of cyclical effects in credit risk measurement model. Basel, Switzerland: Bank for International Settlements, Monetary and Economic Dept., 2003.

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16

Maderbacher, Michael. Früherkennung von Kreditrisken: Dynamische Kontendatenanalyse zur Risikofrüherkennung. Wien: Orac, 1998.

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17

Kiŏp wihŏm kwa sinyong pʻyŏngka. Sŏul: Hanhaksa, 1986.

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18

John, Bilardello, ed. Fundamentals of corporate credit analysis. New York: McGraw-Hill, 2005.

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19

Nationalbank, Oesterreichische. Guidelines on bank-wide risk management: Internal capital adequacy assessment process. Vienna: Oesterreichische Nationalbank, 2006.

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20

Ökologische Risiken in der Kreditwürdigkeitsprüfung. Frankfurt am Main: P. Lang, 1994.

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21

Silver, Lars. Credit risk assessment in different contexts: The influence of local networks for bank financing of SMEs. Uppsala: Dept. of Business Studies, Uppsala University, 2001.

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22

Pryce, Gwilym B. J. The impact of debt crises on lenders' weighting of risk signals: Asymmetric information in the credit market and bank assessment of risk. Glasgow: University of Glasgow, Centre for Housing Research and Urban Studies, 1996.

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23

Pryce, Gwilym B. J. The impact of debt crises on lenders' weighting of risk signals: Asymmetric information in the credit market and bank assessment of risk. Glasgow: Centre for Housing Research and Urban Studies, 1997.

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24

Kaiser, Harry Mason. A risk analysis of farm program participation. [St. Paul]: Minnesota Agricultural Experiment Station, University of Minnesota, 1987.

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25

Segoviano, Miguel A. Internal ratings, the business cycle and capital requirements: Some evidence from an emerging market economy. Basel, Switzerland: Bank for International Settlements, Monetary and Economic Dept., 2002.

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26

John, Hull. Hull-White on derivatives: A compilation of articles. London: Risk Publications, 1996.

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27

Pryce, Gwilym. Asymmetric information in the international credit market and bank assessment of country risk: The impact of the debtcrisis and the secondary market on lenders' weighting of signals. [s.l.]: typescript, 1993.

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28

Abrahams, Clark, and Mingyuan Zhang, eds. Credit Risk Assessment. Wiley, 2012. http://dx.doi.org/10.1002/9781119202769.

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29

Laurentis, Giacomo De, Renato Maino, and Luca Molteni. Developing, Validating and Using Internal Ratings: Methodologies and Case Studies. Wiley & Sons, Incorporated, John, 2010.

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30

Laurentis, Giacomo De, Renato Maino, and Luca Molteni. Developing, Validating and Using Internal Ratings: Methodologies and Case Studies. Wiley & Sons, Incorporated, John, 2011.

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31

Laurentis, Giacomo De, Renato Maino, and Luca Molteni. Developing, Validating and Using Internal Ratings: Methodologies and Case Studies. Wiley & Sons, Incorporated, John, 2011.

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32

G, Avesani Renzo, ed. Review and implementation of credit risk models of the financial sector assessment program. [Washington, D.C.?]: International Monetary Fund, 2006.

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33

International Trade Centre UNCTAD/WTO. Division of Trade Support Services., ed. How to evaluate trade credit requests: A guide for bankers and the scorecard : a risk analysis tool. Geneva: ITC, 1999.

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34

Zopounidis, Constantin, Michalis Doumpos, Christos Lemonakis, and Dimitrios Niklis. Analytical Techniques in the Assessment of Credit Risk: An Overview of Methodologies and Applications. Springer, 2018.

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35

Zopounidis, Constantin, Michalis Doumpos, Christos Lemonakis, and Dimitrios Niklis. Analytical Techniques in the Assessment of Credit Risk: An Overview of Methodologies and Applications. Springer, 2018.

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36

Ganguin, Blaise, and John Bilardello. Standard & Poor's Fundamentals of Corporate Credit Analysis. McGraw-Hill, 2004.

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37

Ganguin, Blaise, and John Bilardello. Standard & Poor's Fundamentals of Corporate Credit Analysis. McGraw-Hill, 2004.

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38

Stress Testing for Risk Control Under Basel II. Butterworth-Heinemann, 2006.

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39

White, Alan, and John Hull. Hull-White On Derivatives. Risk Books, 1996.

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40

Besedovsky, Natalia. Uncertain Meanings of Risk. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198820802.003.0011.

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This chapter studies calculative risk-assessment practices in credit rating agencies. It identifies two fundamentally different methodological approaches for producing ratings, which in turn shape the respective conceptions of credit risk. The traditional approach sees ‘risk’ as an only partially calculable and predictable set of hazards that should be avoided or minimized. This approach is particularly evident in the production of country credit ratings and gives rise to ordinal rankings of risk. By contrast, structured finance rating practices conceive of ‘risk’ as both fully calculable and controllable; they construct cardinal measures of risk by assuming that ontological uncertainty does not exist and that models can capture all possible events in a probabilistic manner. This assumption—that uncertainty can be turned into measurable risk—is a necessary precondition for structured finance securities and has become an influential imaginary in financial markets.
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41

Fiordelisi, Franco, Corrado Meglio, Carlo Palego, Annalissa Richetto, Artem Danko, Maurizio Vallino, Pasqualina Porretta, Lorenzo Bocchi, Carlo Toffano, and Andrea Favretti. Pricing and risk adjusted measures. AIFIRM, 2021. http://dx.doi.org/10.47473/2016ppa00027.

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The issue of risk-based pricing of credit loans has become crucial for banking companies, in a context characterized by severe restriction of profitability margins also in relation to a level of market interest rates which in the Euro area is at its lowest. historical, now firmly in the negative area. The same European Authorities urge the adoption of adequate and consistent adjusted pricing frameworks with respect to the business model, risk profile and overall risk governance of the bank. The methodological and organizational process for determining the risk-adjusted pricing is further complicated by the ongoing Covid19 pandemic which, through the highly asymmetrical impacts on customer segments and industrial sectors, makes the forward-looking and macroeconomic assessment of the sectors risk even more relevant.
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42

Tuite, Cl´ıodhna, Michael O’Neill, and Anthony Brabazon. Economic and Financial Modeling with Genetic Programming. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.10.

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This chapter focuses on genetic programming (GP), a stochastic optimization and model induction technique. An advantage of GP is that the modeler need not select the exact parameters to be used in the model beforehand. Rather, GP can effectively search a complex model space defined by a set of building blocks specified by the modeler. This flexibility has allowed GP to be used for many applications. The chapter reviews some of the most significant developments using GP: forecasting, stock selection, derivative pricing and trading, bankruptcy and credit risk assessment, and agent-based and economic modeling. Conclusions reached by studies investigating similar problems do not always agree; however, GP has proved useful across a wide range of problem areas. Recent and future work is increasingly concerned with adapting genetic programming to more dynamic environments and ensuring that solutions generalize robustly to out-of-sample data, to further improve model performance.
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