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Author: Massimo Morini
Publisher: John Wiley & Sons
ISBN: 0470977744
Category : Business & Economics
Languages : en
Pages : 452
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Book Description
A guide to the validation and risk management of quantitative models used for pricing and hedging Whereas the majority of quantitative finance books focus on mathematics and risk management books focus on regulatory aspects, this book addresses the elements missed by this literature--the risks of the models themselves. This book starts from regulatory issues, but translates them into practical suggestions to reduce the likelihood of model losses, basing model risk and validation on market experience and on a wide range of real-world examples, with a high level of detail and precise operative indications.
Author: Massimo Morini
Publisher: John Wiley & Sons
ISBN: 0470977744
Category : Business & Economics
Languages : en
Pages : 452
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Book Description
A guide to the validation and risk management of quantitative models used for pricing and hedging Whereas the majority of quantitative finance books focus on mathematics and risk management books focus on regulatory aspects, this book addresses the elements missed by this literature--the risks of the models themselves. This book starts from regulatory issues, but translates them into practical suggestions to reduce the likelihood of model losses, basing model risk and validation on market experience and on a wide range of real-world examples, with a high level of detail and precise operative indications.
Author: Harald Scheule
Publisher:
ISBN: 9781906348250
Category : Credit
Languages : en
Pages : 500
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Book Description
The book aims to provide solutions on how to include model risk into existing risk measurement frameworks. It also aims to provide solutions on how to build models of higher accuracy and thus lower model risk.
Author: Rajna Gibson
Publisher: Risk Publications
ISBN:
Category : Business & Economics
Languages : en
Pages : 378
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Book Description
A comprehensive compilation on the concept of model risk and the potential pitfalls associated with modelling financial risks, this book provides an assessment of various models, examining the weaknesses and provides methods to mitigate potential model failures and deficiencies. It also covers the testing of models, what should be tested and what the parameters should be, with core contributions selected and introduced by Professor Rajna Gibson.
Author: Jack Freund
Publisher: Butterworth-Heinemann
ISBN: 0127999329
Category : Computers
Languages : en
Pages : 411
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Book Description
Using the factor analysis of information risk (FAIR) methodology developed over ten years and adopted by corporations worldwide, Measuring and Managing Information Risk provides a proven and credible framework for understanding, measuring, and analyzing information risk of any size or complexity. Intended for organizations that need to either build a risk management program from the ground up or strengthen an existing one, this book provides a unique and fresh perspective on how to do a basic quantitative risk analysis. Covering such key areas as risk theory, risk calculation, scenario modeling, and communicating risk within the organization, Measuring and Managing Information Risk helps managers make better business decisions by understanding their organizational risk. - Uses factor analysis of information risk (FAIR) as a methodology for measuring and managing risk in any organization. - Carefully balances theory with practical applicability and relevant stories of successful implementation. - Includes examples from a wide variety of businesses and situations presented in an accessible writing style.
Author: Yacov Y. Haimes
Publisher: John Wiley & Sons
ISBN: 1118210921
Category : Technology & Engineering
Languages : en
Pages : 810
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Book Description
Examines timely multidisciplinary applications, problems, and case histories in risk modeling, assessment, and management Risk Modeling, Assessment, and Management, Third Edition describes the state of the art of risk analysis, a rapidly growing field with important applications in engineering, science, manufacturing, business, homeland security, management, and public policy. Unlike any other text on the subject, this definitive work applies the art and science of risk analysis to current and emergent engineering and socioeconomic problems. It clearly demonstrates how to quantify risk and construct probabilities for real-world decision-making problems, including a host of institutional, organizational, and political issues. Avoiding higher mathematics whenever possible, this important new edition presents basic concepts as well as advanced material. It incorporates numerous examples and case studies to illustrate the analytical methods under discussion and features restructured and updated chapters, as well as: A new chapter applying systems-driven and risk-based analysis to a variety of Homeland Security issues An accompanying FTP site—developed with Professor Joost Santos—that offers 150 example problems with an Instructor's Solution Manual and case studies from a variety of journals Case studies on the 9/11 attack and Hurricane Katrina An adaptive multiplayer Hierarchical Holographic Modeling (HHM) game added to Chapter Three This is an indispensable resource for academic, industry, and government professionals in such diverse areas as homeland and cyber security, healthcare, the environment, physical infrastructure systems, engineering, business, and more. It is also a valuable textbook for both undergraduate and graduate students in systems engineering and systems management courses with a focus on our uncertain world.
Author: Constantin Zopounidis
Publisher: Springer Nature
ISBN: 3030666913
Category : Business & Economics
Languages : en
Pages : 480
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Book Description
Risk is the main source of uncertainty for investors, debtholders, corporate managers and other stakeholders. For all these actors, it is vital to focus on identifying and managing risk before making decisions. The success of their businesses depends on the relevance of their decisions and consequently, on their ability to manage and deal with the different types of risk. Accordingly, the main objective of this book is to promote scientific research in the different areas of risk management, aiming at being transversal and dealing with different aspects of risk management related to corporate finance as well as market finance. Thus, this book should provide useful insights for academics as well as professionals to better understand and assess the different types of risk.
Author: SAS
Publisher: SAS Institute
ISBN: 1970170654
Category : Computers
Languages : en
Pages : 121
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Book Description
Cut through the complexity of model risk management with a guide to solutions from SAS! There is an increasing demand for more model governance and model risk awareness. At the same time, high-performing models are expected to be deployed faster than ever. SAS Model Risk Management is a user-friendly, web-based application that facilitates the capture and life cycle management of statistical model-related information. It enables all stakeholders in the model life cycle — developers, validators, internal audit, and management – to get overview reports as well as detailed information in one central place. Model Risk Management with SAS introduces you to the features and capabilities of this software, including the entry, collection, transfer, storage, tracking, and reporting of models that are drawn from multiple lines of business across an organization. This book teaches key concepts, terminology, and base functionality that are integral to SAS Model Risk Management through hands-on examples and demonstrations. With this guide to SAS Model Risk Management, your organization can be confident it is making fact-based decisions and mitigating model risk.
Author: Morton Glantz
Publisher: Academic Press
ISBN: 0124016944
Category : Business & Economics
Languages : en
Pages : 545
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Book Description
Multi-Asset Risk Modeling describes, in a single volume, the latest and most advanced risk modeling techniques for equities, debt, fixed income, futures and derivatives, commodities, and foreign exchange, as well as advanced algorithmic and electronic risk management. Beginning with the fundamentals of risk mathematics and quantitative risk analysis, the book moves on to discuss the laws in standard models that contributed to the 2008 financial crisis and talks about current and future banking regulation. Importantly, it also explores algorithmic trading, which currently receives sparse attention in the literature. By giving coherent recommendations about which statistical models to use for which asset class, this book makes a real contribution to the sciences of portfolio management and risk management. - Covers all asset classes - Provides mathematical theoretical explanations of risk as well as practical examples with empirical data - Includes sections on equity risk modeling, futures and derivatives, credit markets, foreign exchange, and commodities
Author: Karan Girotra
Publisher: Harvard Business Review Press
ISBN: 1422191540
Category : Business & Economics
Languages : en
Pages : 251
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Book Description
How to outsmart risk Risk has been defined as the potential for losing something of value. In business, that value could be your original investment or your expected future returns. The Risk-Driven Business Model will help you manage risk better by showing how the key choices you make in designing your business models either increase or reduce two characteristic types of risk—information risk, when you make decisions without enough information, and incentive-alignment risk, when decision makers’ incentives are at odds with the broader goals of the company. Leaders who understand how the structure of their business model affects risk have the power to create wealth, revolutionize industries, and shape a better world. INSEAD’s Karan Girotra and Serguei Netessine, noted operations and innovation professors who have consulted with dozens of companies, walk you through a business model audit to determine what key decisions get made in a business, when they get made, who makes them, and why we make the decisions we do. By changing your company’s key decisions within this framework, you can fundamentally alter the risks that will impact your business. This book is for entrepreneurs and executives in companies involved in dynamic industries where the locus of risk is shifting, and includes lessons from Zipcar, Blockbuster, Apple, Benetton, Kickstarter, Walmart, and dozens of other global companies. The Risk-Driven Business Model demystifies business model risk, with clear directives aimed at improving decision making and driving your business forward.
Author: Bart Baesens
Publisher:
ISBN:
Category :
Languages : en
Pages : 283
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Book Description
Get up to speed on identifying and tackling model risk! Managing Model Risk provides data science practitioners, business professionals and analytics managers with a comprehensive guide to understand and tackle the fundamental concept of analytical model risk in terms of data, model specification, model development, model validation, model operationalization, model security and model management. Providing state of the art industry and research insights based on the author''s extensive experience, this illustrated textbook has a well-balanced theory-practice focus and covers all essential topics. Key Features: Extensive coverage of important trending topics and their risk impact on analytical models, starting from the raw data up until the operationalization, security and management. Various examples and case studies to highlight the topics discussed. Key references to background literature for further clarification. An online website with various add-ons and recent developments: www.managingmodelriskbook.com. What Makes this Book Different? This book is based on both authors having worked in analytics for more than 30 years combined, both in industry and academia. Both authors have co-authored more than 300 scientific publications on analytics and machine learning and have worked with firms in different industries, including (online) retailers, financial institutions, manufacturing firms, insurance providers, governments, etc. all over the globe estimating, deploying and validating analytical models. Throughout this time, we have read many books about analytical modeling and data science, which are typically written from the perspective of a theorist, providing lots of details with regards to different model algorithms and related mathematics, but with limited attention being given to how such models are used in practice. If such concerns are tackled, it is mainly from an implementation, use case or data engineering perspective. From our own experience, however, we have encountered many cases where analytics, AI, machine learning etc. fail in organizations, even with skilled people working on them, due to a myriad of reasons: bad data quality, difficulties in terms of model deployment, lack of model buy-in, incorrect definitions of underlying goals, wrong evaluation metrics, unrealistic expectations and many other issues can arise which cause models to fail in practice. Most of these issues have nothing to do with the actual algorithm being used to construct the model, but rather with everything else surrounding it: data, governance, maintenance, business, management, the economy, budgeting, culture etc. As such, we wanted to offer a new perspective with this book: it aims to provide a unique mix of both practical and research-based insights and report on do''s and don''ts for model risk management. Model risk issues are not only highlighted but also recommendations are given on how to deal with them, where possible. Target Audience This book is targeted towards everyone who has previously been exposed to both predictive and descriptive analytics. The reader should hence have some basic understanding of the analytics process model, the key activities of data preprocessing, the steps involved in developing a predictive analytics model (using e.g. linear or logistic regression, decision trees, etc.) and a descriptive analytics model (using e.g. association or sequence rules or clustering techniques). It is also important to be aware of how an analytical model can be properly evaluated, both in terms of accuracy and interpretation. This book aims to offer a comprehensive guide for both data scientists as well as (C-level) executives and data science or engineering leads, decision-makers and managers who want to know the key underlying concepts of analytical model risk.
