Download or read book Understanding and Managing Model Risk written by Massimo Morini and published by John Wiley & Sons. This book was released on 2011-10-20 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Risk Modeling Assessment and Management written by Yacov Y. Haimes and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Model Risk written by Harald Scheule and published by . This book was released on 2010 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Managing Model Risk written by Bart Baesens and published by . This book was released on 2021-06-30 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Measuring and Managing Information Risk written by Jack Freund and published by Butterworth-Heinemann. This book was released on 2014-08-23 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Model Risk Management with SAS written by SAS and published by SAS Institute. This book was released on 2020-06-29 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book The Risk Driven Business Model written by Karan Girotra and published by Harvard Business Review Press. This book was released on 2014-06-10 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Introduction to Credit Risk Modeling written by Christian Bluhm and published by CRC Press. This book was released on 2016-04-19 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains Nearly 100 Pages of New MaterialThe recent financial crisis has shown that credit risk in particular and finance in general remain important fields for the application of mathematical concepts to real-life situations. While continuing to focus on common mathematical approaches to model credit portfolios, Introduction to Credit Risk Modelin
Download or read book Bayesian Risk Management written by Matt Sekerke and published by John Wiley & Sons. This book was released on 2015-09-15 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Risk Topography written by Markus Brunnermeier and published by University of Chicago Press. This book was released on 2014-10-17 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent financial crisis and the difficulty of using mainstream macroeconomic models to accurately monitor and assess systemic risk have stimulated new analyses of how we measure economic activity and the development of more sophisticated models in which the financial sector plays a greater role. Markus Brunnermeier and Arvind Krishnamurthy have assembled contributions from leading academic researchers, central bankers, and other financial-market experts to explore the possibilities for advancing macroeconomic modeling in order to achieve more accurate economic measurement. Essays in this volume focus on the development of models capable of highlighting the vulnerabilities that leave the economy susceptible to adverse feedback loops and liquidity spirals. While these types of vulnerabilities have often been identified, they have not been consistently measured. In a financial world of increasing complexity and uncertainty, this volume is an invaluable resource for policymakers working to improve current measurement systems and for academics concerned with conceptualizing effective measurement.
Download or read book Multi Asset Risk Modeling written by Morton Glantz and published by Academic Press. This book was released on 2013-12-03 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Risk Modeling for Hazards and Disasters written by Gero Michel and published by Elsevier. This book was released on 2017-08-29 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Risk Modeling for Hazards and Disasters covers all major aspects of catastrophe risk modeling, from hazards through to financial analysis. It explores relevant new science in risk modeling, indirect losses, assessment of impact and consequences to insurance losses, and current changes in risk modeling practice, along with case studies. It also provides further insight into the shortcomings of current models and examines model risk and ideas to diversify risk assessment. Risk Modeling for Hazards and Disasters instructs readers on how to assess, price and then hedge the losses from natural and manmade catastrophes. This book reviews current model development and science and explains recent changes in the catastrophe modeling space, including new initiatives covering uncertainty and big data in the assessment of risk for insurance pricing and portfolio management. Edited by a leading expert in both hazards and risk, this book is authored by a global panel including major modeling vendors, modeling consulting firms, and well-known catastrophe modeling scientists. Risk Modeling for Hazards and Disasters provides important insight into how models are used to price and manage risk. - Includes high profile case studies such as the Newcastle earthquake, Hurricane Andrew and Hurricane Katrina - Provides crucial information on new ideas and platforms that will help address the new demands for risk management and catastrophe risk reporting - Presents the theory and practice needed to know how models are created and what is and what is not important in the modeling process - Covers relevant new science in risk modeling, indirect losses, assessment of impact and consequences to insurance losses, and current changes in risk modeling practice, along with case studies
Download or read book The Analytics of Risk Model Validation written by George A. Christodoulakis and published by Elsevier. This book was released on 2007-11-14 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Risk model validation is an emerging and important area of research, and has arisen because of Basel I and II. These regulatory initiatives require trading institutions and lending institutions to compute their reserve capital in a highly analytic way, based on the use of internal risk models. It is part of the regulatory structure that these risk models be validated both internally and externally, and there is a great shortage of information as to best practise. Editors Christodoulakis and Satchell collect papers that are beginning to appear by regulators, consultants, and academics, to provide the first collection that focuses on the quantitative side of model validation. The book covers the three main areas of risk: Credit Risk and Market and Operational Risk.*Risk model validation is a requirement of Basel I and II *The first collection of papers in this new and developing area of research *International authors cover model validation in credit, market, and operational risk
Download or read book Model Driven Risk Analysis written by Mass Soldal Lund and published by Springer Science & Business Media. This book was released on 2010-10-20 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: The term “risk” is known from many fields, and we are used to references to contractual risk, economic risk, operational risk, legal risk, security risk, and so forth. We conduct risk analysis, using either offensive or defensive approaches to identify and assess risk. Offensive approaches are concerned with balancing potential gain against risk of investment loss, while defensive approaches are concerned with protecting assets that already exist. In this book, Lund, Solhaug and Stølen focus on defensive risk analysis, and more explicitly on a particular approach called CORAS. CORAS is a model-driven method for defensive risk analysis featuring a tool-supported modelling language specially designed to model risks. Their book serves as an introduction to risk analysis in general, including the central concepts and notions in risk analysis and their relations. The authors’ aim is to support risk analysts in conducting structured and stepwise risk analysis. To this end, the book is divided into three main parts. Part I of the book introduces and demonstrates the central concepts and notation used in CORAS, and is largely example-driven. Part II gives a thorough description of the CORAS method and modelling language. After having completed this part of the book, the reader should know enough to use the method in practice. Finally, Part III addresses issues that require special attention and treatment, but still are often encountered in real-life risk analysis and for which CORAS offers helpful advice and assistance. This part also includes a short presentation of the CORAS tool support. The main target groups of the book are IT practitioners and students at graduate or undergraduate level. They will appreciate a concise introduction into the emerging field of risk analysis, supported by a sound methodology, and completed with numerous examples and detailed guidelines.
Download or read book Model Risk Management written by Ludger Rüschendorf and published by Cambridge University Press. This book was released on 2024-01-31 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop the tools to quantify model risk, to study its effects in finance, insurance, and engineering, and to reduce it.
Download or read book Interest Rate Risk Modeling written by Sanjay K. Nawalkha and published by John Wiley & Sons. This book was released on 2005-05-31 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: The definitive guide to fixed income valuation and risk analysis The Trilogy in Fixed Income Valuation and Risk Analysis comprehensively covers the most definitive work on interest rate risk, term structure analysis, and credit risk. The first book on interest rate risk modeling examines virtually every well-known IRR model used for pricing and risk analysis of various fixed income securities and their derivatives. The companion CD-ROM contain numerous formulas and programming tools that allow readers to better model risk and value fixed income securities. This comprehensive resource provides readers with the hands-on information and software needed to succeed in this financial arena.
Download or read book Advances in Credit Risk Modeling and Management written by Frédéric Vrins and published by MDPI. This book was released on 2020-07-01 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit risk remains one of the major risks faced by most financial and credit institutions. It is deeply connected to the real economy due to the systemic nature of some banks, but also because well-managed lending facilities are key for wealth creation and technological innovation. This book is a collection of innovative papers in the field of credit risk management. Besides the probability of default (PD), the major driver of credit risk is the loss given default (LGD). In spite of its central importance, LGD modeling remains largely unexplored in the academic literature. This book proposes three contributions in the field. Ye & Bellotti exploit a large private dataset featuring non-performing loans to design a beta mixture model. Their model can be used to improve recovery rate forecasts and, therefore, to enhance capital requirement mechanisms. François uses instead the price of defaultable instruments to infer the determinants of market-implied recovery rates and finds that macroeconomic and long-term issuer specific factors are the main determinants of market-implied LGDs. Cheng & Cirillo address the problem of modeling the dependency between PD and LGD using an original, urn-based statistical model. Fadina & Schmidt propose an improvement of intensity-based default models by accounting for ambiguity around both the intensity process and the recovery rate. Another topic deserving more attention is trade credit, which consists of the supplier providing credit facilities to his customers. Whereas this is likely to stimulate exchanges in general, it also magnifies credit risk. This is a difficult problem that remains largely unexplored. Kanapickiene & Spicas propose a simple but yet practical model to assess trade credit risk associated with SMEs and microenterprises operating in Lithuania. Another topical area in credit risk is counterparty risk and all other adjustments (such as liquidity and capital adjustments), known as XVA. Chataignier & Crépey propose a genetic algorithm to compress CVA and to obtain affordable incremental figures. Anagnostou & Kandhai introduce a hidden Markov model to simulate exchange rate scenarios for counterparty risk. Eventually, Boursicot et al. analyzes CoCo bonds, and find that they reduce the total cost of debt, which is positive for shareholders. In a nutshell, all the featured papers contribute to shedding light on various aspects of credit risk management that have, so far, largely remained unexplored.
