國立虎尾科技大學 |

Credit-risk modelling = theoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Credit-risk modelling/ by David Jamieson Bolder.
Reminder of title:	theoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python /
Author:	Bolder, David Jamieson.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xxxv, 684 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Credit - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-319-94688-7
ISBN:	9783319946887

Credit-risk modelling = theoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python /
Bolder, David Jamieson.

Credit-risk modellingtheoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python /[electronic resource] :by David Jamieson Bolder. - Cham :Springer International Publishing :2018. - xxxv, 684 p. :ill., digital ;24 cm.

Getting 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.

The 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.

ISBN: 9783319946887

Standard No.: 10.1007/978-3-319-94688-7doiSubjects--Topical Terms:

784576
Credit
--Mathematical models.

LC Class. No.: HG3751 / .B653 2018

Dewey Class. No.: 658.880151

Credit-risk modelling = theoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python /
LDR:02795nam a2200325 a 4500 001 929653
003 DE-He213
005 20190322135803.0
006 m d
007 cr nn 008maaau
008 190626s2018 gw s 0 eng d
020 $a 9783319946887 $q (electronic bk.)
020 $a 9783319946870 $q (paper)
024 7 $a 10.1007/978-3-319-94688-7 $2 doi
035 $a 978-3-319-94688-7
040 $a GP $c GP
041 0 $a eng
050 4 $a HG3751 $b .B653 2018
072 7 $a KJM $2 bicssc
072 7 $a BUS033070 $2 bisacsh
072 7 $a KJM $2 thema
082 0 4 $a 658.880151 $2 23
090 $a HG3751 $b .B687 2018
100 1 $a Bolder, David Jamieson. $3 1066206
245 1 0 $a Credit-risk modelling $h [electronic resource] : $b theoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python / $c by David Jamieson Bolder.
260 $a Cham : $c 2018. $b Springer International Publishing : $b Imprint: Springer,
300 $a xxxv, 684 p. : $b ill., digital ; $c 24 cm.
505 0 $a Getting 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.
520 $a The 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.
650 0 $a Credit $x Mathematical models. $3 784576
650 0 $a Financial risk management $x Mathematical models. $3 560395
650 0 $a Python (Computer program language) $3 566246
650 1 4 $a Risk Management. $3 569483
650 2 4 $a Business Finance. $3 1069042
650 2 4 $a Quantitative Finance. $3 669372
650 2 4 $a Financial Engineering. $3 1107684
650 2 4 $a Banking. $2 bicssc $3 810653
650 2 4 $a Statistics for Business/Economics/Mathematical Finance/Insurance. $3 669275
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-3-319-94688-7
950 $a Economics and Finance (Springer-41170)