Understanding And Managing Model Risk

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.

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.

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.

Despite many years of development, risk management remains problematic for the majority of organizations. One common challenge is the human dimension, in other words, the way people perceive risk and risk management. Risk management processes and techniques are operated by people, each of whom is a complex individual, influenced by many different factors. And the problem is compounded by the fact that most risk management involves people working in groups. This introduces further layers of complexity through relationships and group dynamics. David Hillson's and Ruth Murray-Webster's Understanding and Managing Risk Attitude will help you understand the human aspects of risk management and to manage proactively the influence of human behaviour on the risk process. The authors introduce a range of models, perspectives and examples to define and detail the range of possible risk attitudes; looking both at individuals and groups. Using leading-edge thinking on self-awareness and emotional literacy, they develop a powerful approach to address the most common shortfall in current risk management: the failure to manage the human aspects of the process. All this is presented in a practical and applied framework, rather than as a theoretical or academic treatise, based on the authors' shared experiences and expertise, rather than empirical research. Anyone involved in implementing risk management will benefit from this book, including risk practitioners, senior managers and directors responsible for corporate governance, project managers and their teams. It is also essential reading for HR professionals and others interested in organizational or behavioural psychology. This second edition is updated to strengthen the understanding of individual risk attitudes and reinforce what individuals can do to manage those risk attitudes that are leading them away from their objectives. For people who want to embrace this subject, the book highlights ways forward that are proven and practical.

Sound risk management often involves a combination of both mathematical and practical aspects. Taking this into account, Understanding Risk: The Theory and Practice of Financial Risk Management explains how to understand financial risk and how the severity and frequency of losses can be controlled. It combines a quantitative approach with a

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.

Financial risk management is a topic of primary importance in financial markets. It is important to learn how to measure and control risk, how to be primed for the opportunity of compensative return, and how to avoid useless exposure.

CONSTRUCTION RISK MANAGEMENT DECISION MAKING Explores the relevance of systems thinking and behavioral science in construction risk management Effective risk management is a vital component of all successful construction projects. Although quantitative tools for evaluating data and minimizing risk are readily available, construction managers commonly adopt a more innate, experience-based approach. In Construction Risk Management Decision Making, project manager and senior consultant Alex C. Arthur provides step-by-step advice on assessing and prioritizing risk using qualitative decision-making systems in the construction industry. Incorporating key theories and concepts from systems thinking and behavioral science, this highly practical guide focuses on the behavior patterns of real people in the industry, rather than complex quantitative techniques and data. Concise, easy-to-understand chapters highlight the current practices of construction risk management while helping readers view risk and decision making from a broader perspective. Throughout the book, the author presents invaluable insights into the ways construction professionals think and behave in the real world. Addresses the actual risk management practices of construction professionals Applies human behavioral theories to the study of construction risk management decision making Illustrates the highly intuitive approaches prevalent in various construction projects Features real-life case studies and practical examples throughout Construction Risk Management Decision Making is an excellent textbook for advanced students in project management, engineering, construction, and surveying courses, and a must-have guide for practitioners of construction management, surveying, and architecture.