000			05115nam a22002537a 4500
005			20250103180316.0
008			250103b |||||||| |||| 00| 0 eng d
020			_a9781944660659
082			_a658.15 _bORL
100			_aOrlando, Giuseppe _920098
245			_aModern financial engineering: _bcounterparty, credit, portfolio and systemic risks
260			_bWorld Scientific Publishing _aSingapore _c2023
300			_axxv, 406 p.
365			_aINR _b1795.00
500			_aTable of content; Mathematical and Statistical Foundations: Distributions Commonly Used in Credit and Counterparty Risk Modeling Poisson Processes Estimation Techniques Finance Background and Regulatory Framework: Basic Definitions Banking Regulation Before the Crisis The Financial Crisis of the XXI-st Century Credit Risk Regulation After the Crisis Credit Risk Modeling Essentials: Probability of Default (PD) Loss Given Default (LGD) Other Credit Risk Components and Portfolio Risk Model Validation and Audit Counterparty Risk Modeling: EAD Modeling EAD-Related Issues Correlation-Driven Issues Portfolio Credit Risk Management Applications: Credit Risk Models Sector Analysis Estimating PD and LGD for Modeling Non-Performing Loans: The Case of Italy The Case of Italy Credit Default Swap (CDS) Systemic Risk Implications: Diversifying the Economy for Systemic Risk Reduction: The Case of the Kingdom of Saudi Arabia (KSA) Systemic Risk Regulation Appendices: Financial Engineering: Coding in R Financial Engineering: Coding in Matlab Dataset Used for Modeling Non-Performing Loans Readership: Academics and practitioners interested in modern financial engineering. [Mathematical and Statistical Foundations: Distributions Commonly Used in Credit and Counterparty Risk Modeling Poisson Processes Estimation Techniques Finance Background and Regulatory Framework: Basic Definitions Banking Regulation Before the Crisis The Financial Crisis of the XXI-st Century Credit Risk Regulation After the Crisis Credit Risk Modeling Essentials: Probability of Default (PD) Loss Given Default (LGD) Other Credit Risk Components and Portfolio Risk Model Validation and Audit Counterparty Risk Modeling: EAD Modeling EAD-Related Issues Correlation-Driven Issues Portfolio Credit Risk Management Applications: Credit Risk Models Sector Analysis Estimating PD and LGD for Modeling Non-Performing Loans: The Case of Italy The Case of Italy Credit Default Swap (CDS) Systemic Risk Implications: Diversifying the Economy for Systemic Risk Reduction: The Case of the Kingdom of Saudi Arabia (KSA) Systemic Risk Regulation Appendices: Financial Engineering: Coding in R Financial Engineering: Coding in Matlab Dataset Used for Modeling Non-Performing Loans Readership: Academics and practitioners interested in modern financial engineering. [https://www.worldscientific.com/worldscibooks/10.1142/12725?srsltid=AfmBOopM6GPxXGhWxxtLaRhkDmvxhGwuBuw1LhSnWn_6kRI_LCSBFcFc#t=aboutBook]
520			_aThe book offers an overview of credit risk modeling and management. A three-step approach is adopted with the contents, after introducing the essential concepts of both mathematics and finance. Initially the focus is on the modeling of credit risk parameters mainly at the level of individual debtor and transaction, after which the book delves into counterparty credit risk, thus providing the link between credit and market risks. The second part is aimed at the portfolio level when multiple loans are pooled and default correlation becomes an important factor to consider and model. In this respect, the book explains how copulas help in modeling. The final stage is the macro perspective when the combination of credit risks related to financial institutions produces systemic risk and affects overall financial stability. The entire approach is two-dimensional as well. First, all modeling steps have replicable programming codes both in R and Matlab. In this way, the reader can experience the impact of changing the default probabilities of a given borrower or the weights of a sector. Second, at each stage, the book discusses the regulatory environment. This is because, at times, regulation can have stricter constraints than the outcome of internal models. In summary, the book guides the reader in modeling and managing credit risk by providing both the theoretical framework and the empirical tools necessary for a modern finance professional. In this sense, the book is aimed at a wide audience in all fields of study: from quants who want to engage in finance to economists who want to learn about coding and modern financial engineering. (https://www.worldscientific.com/worldscibooks/10.1142/12725?srsltid=AfmBOopM6GPxXGhWxxtLaRhkDmvxhGwuBuw1LhSnWn_6kRI_LCSBFcFc#t=aboutBook)
650			_aFinancial engineering
650			_aRisk management-- Mathematical models _95828
650			_aCredit --Management --Mathematical models _920099
700			_aBufalo, Michele _920100
700			_aPenikas, Henry _920101
700			_aZurlo, Concetta _920102
942			_cBK _2ddc
999			_c7747 _d7747