Risk Metrics Examples
Download Risk Metrics Examples PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Risk Metrics Examples book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Market Risk Analysis, Value at Risk Models
Written by leading market risk academic, Professor Carol Alexander, Value-at-Risk Models forms part four of the Market Risk Analysis four volume set. Building on the three previous volumes this book provides by far the most comprehensive, rigorous and detailed treatment of market VaR models. It rests on the basic knowledge of financial mathematics and statistics gained from Volume I, of factor models, principal component analysis, statistical models of volatility and correlation and copulas from Volume II and, from Volume III, knowledge of pricing and hedging financial instruments and of mapping portfolios of similar instruments to risk factors. A unifying characteristic of the series is the pedagogical approach to practical examples that are relevant to market risk analysis in practice. All together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study. Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the the accompanying CD-ROM . Empirical examples and case studies specific to this volume include: Parametric linear value at risk (VaR)models: normal, Student t and normal mixture and their expected tail loss (ETL); New formulae for VaR based on autocorrelated returns; Historical simulation VaR models: how to scale historical VaR and volatility adjusted historical VaR; Monte Carlo simulation VaR models based on multivariate normal and Student t distributions, and based on copulas; Examples and case studies of numerous applications to interest rate sensitive, equity, commodity and international portfolios; Decomposition of systematic VaR of large portfolios into standard alone and marginal VaR components; Backtesting and the assessment of risk model risk; Hypothetical factor push and historical stress tests, and stress testing based on VaR and ETL.
Enterprise Risk Management
Author: John R. S. Fraser
language: en
Publisher: John Wiley & Sons
Release Date: 2021-07-07
Unlock the incredible potential of enterprise risk management There has been much evolution in terms of ERM best practices, experience, and standards and regulation over the past decade. Enterprise Risk Management: Today’s Leading Research and Best Practices for Tomorrow’s Executives, Second Edition is the revised and updated essential guide to the now immensely popular topic of enterprise risk management (ERM). With contributions from leading academics and practitioners, this book offers insights into what practitioners are doing and what the future holds. You’ll discover how you can implement best practices, improve ERM tools and techniques, and even learn to teach ERM. Retaining the holistic approach to ERM that made the first edition such a success, this new edition adds coverage of new topics including cybersecurity risk, ERM in government, foreign exchange risk, risk appetite, innovation risk, outsourcing risk, scenario planning, climate change risk, and much more. In addition, the new edition includes important updates and enhancements to topics covered in the first edition; so much of it has been revised and enhanced that it is essentially an entirely new book. Enterprise Risk Management introduces you to the concepts and techniques that allow you to identify risks and prioritize the appropriate responses. This invaluable guide offers a broad overview, covering key issues while focusing on the principles that drive effective decision making and determine business success. This comprehensive resource also provides a thorough introduction to ERM as it relates to credit, market, and operational risk, as well as the evolving requirements of the board of directors’ role in overseeing ERM. Through the comprehensive chapters and leading research and best practices covered, this book: Provides a holistic overview of key topics in ERM, including the role of the chief risk officer, development and use of key risk indicators and the risk-based allocation of resources Contains second-edition updates covering additional material related to teaching ERM, risk frameworks, risk culture, credit and market risk, risk workshops and risk profiles and much more. Over 90% of the content from the first edition has been revised or enhanced Reveals how you can prudently apply ERM best practices within the context of your underlying business activities Filled with helpful examples, tables, and illustrations, Enterprise Risk Management, Second Edition offers a wealth of knowledge on the drivers, the techniques, the benefits, as well as the pitfalls to avoid, in successfully implementing ERM.
Risk Analytics
The 2022 World Economic Forum surveyed 1,000 experts and leaders who indicated their risk perception that the earth’s conditions for humans are a main concern in the next 10 years. This means environmental risks are a priority to study in a formal way. At the same time, innovation risks are present in theminds of leaders, newknowledge brings new risk, and the adaptation and adoption of risk knowledge is required to better understand the causes and effects can have on technological risks. These opportunities require not only adopting new ways of managing and controlling emerging processes for society and business, but also adapting organizations to changes and managing new risks. Risk Analytics: Data-Driven Decisions Under Uncertainty introduces a way to analyze and design a risk analytics system (RAS) that integrates multiple approaches to risk analytics to deal with diverse types of data and problems. A risk analytics system is a hybrid system where human and artificial intelligence interact with a data gathering and selection process that uses multiple sources to the delivery of guidelines to make decisions that include humans and machines. The RAS system is an integration of components, such as data architecture with diverse data, and a risk analytics process and modeling process to obtain knowledge and then determine actions through the new knowledge that was obtained. The use of data analytics is not only connected to risk modeling and its implementation, but also to the development of the actionable knowledge that can be represented by text in documents to define and share explicit knowledge and guidelines in the organization for strategy implementation. This book moves from a review of data to the concepts of a RAS. It reviews RAS system components required to support the creation of competitive advantage in organizations through risk analytics. Written for executives, analytics professionals, risk management professionals, strategy professionals, and postgraduate students, this book shows a way to implement the analytics process to develop a risk management practice that creates an adaptive competitive advantage under uncertainty.