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010   $a3-319-94688-9
024 7 $a10.1007/978-3-319-94688-7
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100   $a20181031d2018      u|         0
101 0 $aeng
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182   $cc$2rdamedia
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200 10$aCredit-Risk Modelling $eTheoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python /$fby David Jamieson Bolder
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (704 pages)
311   $a3-319-94687-0 
327   $aGetting Started -- Part I Modelling Frameworks -- A Natural First Step.-Mixture or Actuarial Models -- Threshold Models.-The Genesis of Credit-Risk Modelling -- Part II Diagnostic Tools -- A Regulatory Perspective -- Risk Attribution -- Monte Carlo Methods -- Part III Parameter Estimation -- Default Probabilities -- Default and Asset Correlation.
330   $aThe risk of counterparty default in banking, insurance, institutional, and pension-fund portfolios is an area of ongoing and increasing importance for finance practitioners. It is, unfortunately, a topic with a high degree of technical complexity. Addressing this challenge, this book provides a comprehensive and attainable mathematical and statistical discussion of a broad range of existing default-risk models. Model description and derivation, however, is only part of the story. Through use of exhaustive practical examples and extensive code illustrations in the Python programming language, this work also explicitly shows the reader how these models are implemented. Bringing these complex approaches to life by combining the technical details with actual real-life Python code reduces the burden of model complexity and enhances accessibility to this decidedly specialized field of study. The entire work is also liberally supplemented with model-diagnostic, calibration, and parameter-estimation techniques to assist the quantitative analyst in day-to-day implementation as well as in mitigating model risk. Written by an active and experienced practitioner, it is an invaluable learning resource and reference text for financial-risk practitioners and an excellent source for advanced undergraduate and graduate students seeking to acquire knowledge of the key elements of this discipline.
606   $aRisk management
606   $aBusiness enterprises?Finance
606   $aEconomics, Mathematical 
606   $aFinancial engineering
606   $aBanks and banking
606   $aStatistics 
606   $aRisk Management$3https://scigraph.springernature.com/ontologies/product-market-codes/612040
606   $aBusiness Finance$3https://scigraph.springernature.com/ontologies/product-market-codes/512000
606   $aQuantitative Finance$3https://scigraph.springernature.com/ontologies/product-market-codes/M13062
606   $aFinancial Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/612020
606   $aBanking$3https://scigraph.springernature.com/ontologies/product-market-codes/626010
606   $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010
615  0$aRisk management.
615  0$aBusiness enterprises?Finance.
615  0$aEconomics, Mathematical .
615  0$aFinancial engineering.
615  0$aBanks and banking.
615  0$aStatistics .
615 14$aRisk Management.
615 24$aBusiness Finance.
615 24$aQuantitative Finance.
615 24$aFinancial Engineering.
615 24$aBanking.
615 24$aStatistics for Business, Management, Economics, Finance, Insurance.
676   $a332.701
700   $aBolder$b David Jamieson$4aut$4http://id.loc.gov/vocabulary/relators/aut$01059228
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910299638903321
996   $aCredit-Risk Modelling$92535447
997   $aUNINA