Credit-Risk Modelling

Demonstrates a broad range of state-of-the-art credit-risk models and underscores their interlinkages

Includes extensive Python code to bring the models, diagnostic tools, and estimation of key inputs parameters to life

Combination of mathematical foundations and practical Python code implementation enriches the reader's understanding and competence in this important field

Autorentext

David Jamieson Bolder is currently head of the World Bank Group's (WBG) model-risk function. Prior to this appointment, he provided analytic support to the Bank for International Settlements' (BIS) treasury and asset-management functions and worked in quantitative roles at the Bank of Canada, the World Bank Treasury, and the European Bank for Reconstruction and Development. He has authored numerous papers, articles, and chapters in books on financial modelling, stochastic simulation, and optimization. He has also published a comprehensive book on fixed-income portfolio analytics. His career has focused on the application of mathematical techniques towards informing decision-making in the areas of sovereign-debt, pension-fund, portfolio-risk, and foreign-reserve management.

Zusammenfassung
"The book is easy to read, the models and techniques are illustrated in detail and with complete derivations, making the volume accessible for self-study." (Claudio Fontana, zbMATH 1422.91012, 2019)

Inhalt

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.