Understanding And Managing Model Risk


Understanding And Managing Model Risk
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Understanding And Managing Model Risk


Understanding And Managing Model Risk
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Author : Massimo Morini
language : en
Publisher: John Wiley & Sons
Release Date : 2011-10-20

Understanding And Managing Model Risk written by Massimo Morini and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-20 with Business & Economics categories.


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.



Managing Model Risk


Managing Model Risk
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Author : Bart Baesens
language : en
Publisher:
Release Date : 2021-06-30

Managing Model Risk written by Bart Baesens and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-30 with categories.


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.



Operational Risk Modeling In Financial Services


Operational Risk Modeling In Financial Services
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Author : Patrick Naim
language : en
Publisher: John Wiley & Sons
Release Date : 2019-05-28

Operational Risk Modeling In Financial Services written by Patrick Naim and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-28 with Business & Economics categories.


Transform your approach to oprisk modelling with a proven, non-statistical methodology Operational Risk Modeling in Financial Services provides risk professionals with a forward-looking approach to risk modelling, based on structured management judgement over obsolete statistical methods. Proven over a decade’s use in significant banks and financial services firms in Europe and the US, the Exposure, Occurrence, Impact (XOI) method of operational risk modelling played an instrumental role in reshaping their oprisk modelling approaches; in this book, the expert team that developed this methodology offers practical, in-depth guidance on XOI use and applications for a variety of major risks. The Basel Committee has dismissed statistical approaches to risk modelling, leaving regulators and practitioners searching for the next generation of oprisk quantification. The XOI method is ideally suited to fulfil this need, as a calculated, coordinated, consistent approach designed to bridge the gap between risk quantification and risk management. This book details the XOI framework and provides essential guidance for practitioners looking to change the oprisk modelling paradigm. Survey the range of current practices in operational risk analysis and modelling Track recent regulatory trends including capital modelling, stress testing and more Understand the XOI oprisk modelling method, and transition away from statistical approaches Apply XOI to major operational risks, such as disasters, fraud, conduct, legal and cyber risk The financial services industry is in dire need of a new standard — a proven, transformational approach to operational risk that eliminates or mitigates the common issues with traditional approaches. Operational Risk Modeling in Financial Services provides practical, real-world guidance toward a more reliable methodology, shifting the conversation toward the future with a new kind of oprisk modelling.



Model Risk


Model Risk
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Author : Harald Scheule
language : en
Publisher:
Release Date : 2010

Model Risk written by Harald Scheule and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Credit categories.


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.



Understanding And Managing Interest Rate Risks


Understanding And Managing Interest Rate Risks
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Author : Ren-Raw Chen
language : en
Publisher: World Scientific
Release Date : 1996

Understanding And Managing Interest Rate Risks written by Ren-Raw Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Business & Economics categories.


The book is a systematic summary of modern term structure theories and how interest rate contingent claims are priced under such theories. This is the first book on such an attempt. The book reviews important term structure models and chooses one model to consistantly demonstrate contingent claim pricing. Well-known models are included and their relationships are thoroughly discussed. The book also provides a complete process of model implementation from parameter estimation to hedging. Examples are provided throughout.



Understanding And Managing Risk Attitude


Understanding And Managing Risk Attitude
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Author : Dr David Hillson
language : en
Publisher: Gower Publishing, Ltd.
Release Date : 2012-03-01

Understanding And Managing Risk Attitude written by Dr David Hillson and has been published by Gower Publishing, Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-01 with Business & Economics categories.


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.



Bayesian Risk Management


Bayesian Risk Management
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Author : Matt Sekerke
language : en
Publisher: John Wiley & Sons
Release Date : 2015-08-19

Bayesian Risk Management written by Matt Sekerke and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-19 with Business & Economics categories.


A risk measurement and management framework that takes model risk seriously Most financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. Bayesian Risk Management details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model-driven forecasts in a way that standard statistical risk measurement does not. And unlike current machine learning-based methods, the framework presented here allows you to measure risk in a fully-Bayesian setting without losing the structure afforded by parametric risk and asset-pricing models. Recognize the assumptions embodied in classical statistics Quantify model risk along multiple dimensions without backtesting Model time series without assuming stationarity Estimate state-space time series models online with simulation methods Uncover uncertainty in workhorse risk and asset-pricing models Embed Bayesian thinking about risk within a complex organization Ignoring uncertainty in risk modeling creates an illusion of mastery and fosters erroneous decision-making. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. Bayesian Risk Management provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.



Mastering Operational Risk


Mastering Operational Risk
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Author : Tony Blunden
language : en
Publisher: Pearson UK
Release Date : 2013-10-03

Mastering Operational Risk written by Tony Blunden and has been published by Pearson UK this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-03 with Business & Economics categories.


A practical guide, from the basic techniques, through to advanced applications, showing you what operational risk is, and how you can manage it. Mastering Operational Risk provides a step-by-step guide from the basic elements of operational risk through to advanced applications of operational risk management. Focusing on practical applications, it gives you the knowledge needed to understand what operational risk is and puts in place a workable way of managing it. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.



Risk Modeling


Risk Modeling
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Author : Terisa Roberts
language : en
Publisher: John Wiley & Sons
Release Date : 2022-09-20

Risk Modeling written by Terisa Roberts and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-20 with Business & Economics categories.


A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume: Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques Covers the basic principles and nuances of feature engineering and common machine learning algorithms Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.



Understanding Risk


Understanding Risk
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Author : David Murphy
language : en
Publisher: CRC Press
Release Date : 2008-04-23

Understanding Risk written by David Murphy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-23 with Business & Economics categories.


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