Modern Financial Engineering: Counterparty, Credit, Portfolio And Systemic Risks [Kietas viršelis]

"The 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 institutionsproduces 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"--

In an overview of credit risk modeling and management, Orlando and colleagues introduce the essential concepts of both mathematics and finance, then cover finance background and regulatory framework, credit risk modeling essentials, contemporary risk modeling, portfolio credit risk management applications, and systemic risk implications. Their topics include distributions commonly used in credit and counterparty risk modeling, credit risk regulation: after the crisis, model validation and audit, correlation-driven issues, and credit default swap. Annotation ©2022 Ringgold, Inc., Portland, OR (protoview.com)

The 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.