• A comprehensively updated and reorganized new edition. The updates include comparative methods for improving reliability; methods for optimal allocation of limited resources to achieve a maximum risk reduction; methods for improving reliability at no extra cost and building reliability networks for engineering systems. Includes: A unique set of 46 generic principles for reducing technical risk Monte Carlo simulation algorithms for improving reliability and reducing risk Methods for setting reliability requirements based on the cost of failure New reliability measures based on a minimal separation of random events on a time interval Overstress reliability integral for determining the time to failure caused by overstress failure modes A powerful equation for determining the probability of failure controlled by defects in loaded components with complex shape Comparative methods for improving reliability which do not require reliability data Optimal allocation of limited resources to achieve a maximum risk reduction Improving system reliability based solely on a permutation of interchangeable components
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  • Reliability is one of the most important attributes for the products and processes of any company or organization. This important work provides a powerful framework of domain-independent reliability improvement and risk reducing methods which can greatly lower risk in any area of human activity. It reviews existing methods for risk reduction that can be classified as domain-independent and introduces the following new domain-independent reliability improvement and risk reduction methods: Separation Stochastic separation Introducing deliberate weaknesses Segmentation Self-reinforcement Inversion Reducing the rate of accumulation of damage Permutation Substitution Limiting the space and time exposure Comparative reliability models The domain-independent methods for reliability improvement and risk reduction do not depend on the availability of past failure data, domain-specific expertise or knowledge of the failure mechanisms underlying the failure modes. Through numerous examples and case studies, this invaluable guide shows that many of the new domain-independent methods improve reliability at no extra cost or at a low cost. Using the proven methods in this book, any company and organisation can greatly enhance the reliability of its products and operations.
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  • This complete resource on the theory and applications of reliability engineering, probabilistic models and risk analysis consolidates all the latest research, presenting the most up-to-date developments in this field. With comprehensive coverage of the theoretical and practical issues of both classic and modern topics, it also provides a unique commemoration to the centennial of the birth of Boris Gnedenko, one of the most prominent reliability scientists of the twentieth century. Key features include: expert treatment of probabilistic models and statistical inference from leading scientists, researchers and practitioners in their respective reliability fields detailed coverage of multi-state system reliability, maintenance models, statistical inference in reliability, systemability, physics of failures and reliability demonstration many examples and engineering case studies to illustrate the theoretical results and their practical applications in industry Applied Reliability Engineering and Risk Analysis is one of the first works to treat the important areas of degradation analysis, multi-state system reliability, networks and large-scale systems in one comprehensive volume. It is an essential reference for engineers and scientists involved in reliability analysis, applied probability and statistics, reliability engineering and maintenance, logistics, and quality control. It is also a useful resource for graduate students specialising in reliability analysis and applied probability and statistics. Dedicated to the Centennial of the birth of Boris Gnedenko, renowned Russian mathematician and reliability theorist
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  • This book is based on a lecture course to students specializing in the safety of technological processes and production. The author focuses on three main problems in technological risks and safety: elements of reliability theory, the basic notions, models and methods of general risk theory and some aspects of insurance in the context of risk management. Although the material in this book is aimed at those working towards a bachelors degree in engineering, it may also be of interest to postgraduate students and specialists dealing with problems related to reliability and risks.
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  • A mathematical guide to measuring and managing financial risk. Our modern economy depends on financial markets. Yet financial markets continue to grow in size and complexity. As a result, the management of financial risk has never been more important. Quantitative Financial Risk Management introduces students and risk professionals to financial risk management with an emphasis on financial models and mathematical techniques. Each chapter provides numerous sample problems and end of chapter questions. The book provides clear examples of how these models are used in practice and encourages readers to think about the limits and appropriate use of financial models. Topics include: • Value at risk • Stress testing • Credit risk • Liquidity risk • Factor analysis • Expected shortfall • Copulas • Extreme value theory • Risk model backtesting • Bayesian analysis • . . . and much more
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  • 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.
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  • Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts – Statistical Models and Methods, Statistical Models and Methods in Survival Analysis, and Reliability and Maintenance. The book is intended for researchers interested in statistical methodology and models useful in survival analysis, system reliability and statistical testing for censored and non-censored data.
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  • Book DescriptionIn this volume the methodological aspects of the scenario logic and probabilistic (LP) non-success risk management are considered. The theoretical bases of scenario non-success risk LP-management in business and engineering are also stated. Methods and algorithms for the scenario risk LP-management in problems of classification, investment and effectiveness are described. Risk LP- models and results of numerical investigations for credit risks, risk of frauds, security portfolio risk, risk of quality, accuracy, and risk in multi-stage systems reliability are given. In addition, a rather large number of new problems of estimation, analysis and management of risk are considered. Software for risk problems based on LP-methods, LP-theory, andGIE is described too.
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  • A newly revised and updated edition that details both the theoretical foundations and practical applications of reliability engineering Reliability is one of the most important quality characteristics of components, products, and large and complex systems—but it takes a significant amount of time and resources to bring reliability to fruition. Thoroughly classroom- and industry-tested, this book helps ensure that engineers see reliability success with every product they design, test, and manufacture. Divided into three parts, Reliability Engineering, Second Edition handily describes the theories and their practical uses while presenting readers with real-world examples and problems to solve. Part I focuses on system reliability estimation for time independent and failure dependent models, helping engineers create a reliable design. Part II aids the reader in assembling necessary components and configuring them to achieve desired reliability objectives, conducting reliability tests on components, and using field data from similar components. Part III follows what happens once a product is produced and sold, how the manufacturer must ensure its reliability objectives by providing preventive and scheduled maintenance and warranty policies. This Second Edition includes in-depth and enhanced chapter coverage of: Reliability and Hazard Functions System Reliability Evaluation Time- and Failure-Dependent Reliability Estimation Methods of the Parameters of Failure-Time Distributions Parametric Reliability Models Models for Accelerated Life Testing Renewal Processes and Expected Number of Failures Preventive Maintenance and Inspection Warranty Models Case Studies A comprehensive reference for practitioners and professionals in quality and reliability engineering, Reliability Engineering can also be used for senior undergraduate or graduate courses in industrial and systems, mechanical, and electrical engineering programs.
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  • Practical Approaches to Reliability Theory in Cutting-Edge Applications Probabilistic Reliability Models helps readers understand and properly use statistical methods and optimal resource allocation to solve engineering problems. The author supplies engineers with a deeper understanding of mathematical models while also equipping mathematically oriented readers with a fundamental knowledge of the engineeringrelated applications at the center of model building. The book showcases the use of probability theory and mathematical statistics to solve common, real-world reliability problems. Following an introduction to the topic, subsequent chapters explore key systems and models including: • Unrecoverable objects and recoverable systems • Methods of direct enumeration • Markov models and heuristic models • Performance effectiveness • Time redundancy • System survivability • Aging units and their related systems • Multistate systems Detailed case studies illustrate the relevance of the discussed methods to real-world technical projects including software failure avalanches, gas pipelines with underground storage, and intercontinental ballistic missile (ICBM) control systems. Numerical examples and detailed explanations accompany each topic, and exercises throughout allow readers to test their comprehension of the presented material. Probabilistic Reliability Models is an excellent book for statistics, engineering, and operations research courses on applied probability at the upper-undergraduate and graduate levels. The book is also a valuable reference for professionals and researchers working in industry who would like a mathematical review of reliability models and the relevant applications.
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  • A cutting-edge guide for the theories, applications, and statistical methodologies essential to heavy tailed risk modeling Focusing on the quantitative aspects of heavy tailed loss processes in operational risk and relevant insurance analytics, Advances in Heavy Tailed Risk Modeling: A Handbook of Operational Risk presents comprehensive coverage of the latest research on the theories and applications in risk measurement and modeling techniques. Featuring a unique balance of mathematical and statistical perspectives, the handbook begins by introducing the motivation for heavy tailed risk processes in high consequence low frequency loss modeling. With a companion, Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk, the book provides a complete framework for all aspects of operational risk management and includes: Clear coverage on advanced topics such as splice loss models, extreme value theory, heavy tailed closed form loss distributional approach models, flexible heavy tailed risk models, risk measures, and higher order asymptotic approximations of risk measures for capital estimation An exploration of the characterization and estimation of risk and insurance modelling, which includes sub-exponential models, alpha-stable models, and tempered alpha stable models An extended discussion of the core concepts of risk measurement and capital estimation as well as the details on numerical approaches to evaluation of heavy tailed loss process model capital estimates Numerous detailed examples of real-world methods and practices of operational risk modeling used by both financial and non-financial institutions Advances in Heavy Tailed Risk Modeling: A Handbook of Operational Risk is an excellent reference for risk management practitioners, quantitative analysts, financial engineers, and risk managers. The book is also a useful handbook for graduate-level courses on heavy tailed processes, advanced risk management, and actuarial science.
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  • Never before has risk management been so important. Now in its third edition, this seminal work by Joël Bessis has been comprehensively revised and updated to take into account the changing face of risk management. Fully restructured, featuring new material and discussions on new financial products, derivatives, Basel II, credit models based on time intensity models, implementing risk systems and intensity models of default, it also includes a section on Subprime that discusses the crisis mechanisms and makes numerous references throughout to the recent stressed financial conditions. The book postulates that risk management practices and techniques remain of major importance, if implemented in a sound economic way with proper governance. Risk Management in Banking, Third Edition considers all aspects of risk management emphasizing the need to understand conceptual and implementation issues of risk management and examining the latest techniques and practical issues, including: Asset-Liability Management Risk regulations and accounting standards Market risk models Credit risk models Dependencies modeling Credit portfolio models Capital Allocation Risk-adjusted performance Credit portfolio management Building on the considerable success of this classic work, the third edition is an indispensable text for MBA students, practitioners in banking and financial services, bank regulators and auditors alike.
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  • Never before has risk management been so important. Now in its third edition, this seminal work by Joël Bessis has been comprehensively revised and updated to take into account the changing face of risk management. Fully restructured, featuring new material and discussions on new financial products, derivatives, Basel II, credit models based on time intensity models, implementing risk systems and intensity models of default, it also includes a section on Subprime that discusses the crisis mechanisms and makes numerous references throughout to the recent stressed financial conditions. The book postulates that risk management practices and techniques remain of major importance, if implemented in a sound economic way with proper governance. Risk Management in Banking, Third Edition considers all aspects of risk management emphasizing the need to understand conceptual and implementation issues of risk management and examining the latest techniques and practical issues, including: Asset-Liability Management Risk regulations and accounting standards Market risk models Credit risk models Dependencies modeling Credit portfolio models Capital Allocation Risk-adjusted performance Credit portfolio management Building on the considerable success of this classic work, the third edition is an indispensable text for MBA students, practitioners in banking and financial services, bank regulators and auditors alike.
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  • Offers a holistic approach to guiding product design, manufacturing, and after-sales support as the manufacturing industry transitions from a product-oriented model to service-oriented paradigm This book provides fundamental knowledge and best industry practices in reliability modelling, maintenance optimization, and service parts logistics planning. It aims to develop an integrated product-service system (IPSS) synthesizing design for reliability, performance-based maintenance, and spare parts inventory. It also presents a lifecycle reliability-inventory optimization framework where reliability, redundancy, maintenance, and service parts are jointly coordinated. Additionally, the book aims to report the latest advances in reliability growth planning, maintenance contracting and spares inventory logistics under non-stationary demand condition. Reliability Engineering and Service provides in-depth chapter coverage of topics such as: Reliability Concepts and Models; Mean and Variance of Reliability Estimates; Design for Reliability; Reliability Growth Planning; Accelerated Life Testing and Its Economics; Renewal Theory and Superimposed Renewals; Maintenance and Performance-Based Logistics; Warranty Service Models; Basic Spare Parts Inventory Models; Repairable Inventory Systems; Integrated Product-Service Systems (IPPS), and Resilience Modeling and Planning Guides engineers to design reliable products at a low cost Assists service engineers in providing superior after-sales support Enables managers to respond to the changing market and customer needs Uses end-of-chapter case studies to illustrate industry best practice Lifecycle approach to reliability, maintenance and spares provisioning Reliability Engineering and Service is an important book for graduate engineering students, researchers, and industry-based reliability practitioners and consultants.
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  • Financial risk has become a focus of financial and nonfinancial firms, individuals, and policy makers. But the study of risk remains a relatively new discipline in finance and continues to be refined. The financial market crisis that began in 2007 has highlighted the challenges of managing financial risk. Now, in Financial Risk Management, author Allan Malz addresses the essential issues surrounding this discipline, sharing his extensive career experiences as a risk researcher, risk manager, and central banker. The book includes standard risk measurement models as well as alternative models that address options, structured credit risks, and the real-world complexities or risk modeling, and provides the institutional and historical background on financial innovation, liquidity, leverage, and financial crises that is crucial to practitioners and students of finance for understanding the world today. Financial Risk Management is equally suitable for firm risk managers, economists, and policy makers seeking grounding in the subject. This timely guide skillfully surveys the landscape of financial risk and the financial developments of recent decades that culminated in the crisis. The book provides a comprehensive overview of the different types of financial risk we face, as well as the techniques used to measure and manage them. Topics covered include: Market risk, from Value-at-Risk (VaR) to risk models for options Credit risk, from portfolio credit risk to structured credit products Model risk and validation Risk capital and stress testing Liquidity risk, leverage, systemic risk, and the forms they take Financial crises, historical and current, their causes and characteristics Financial regulation and its evolution in the wake of the global crisis And much more Combining the more model-oriented approach of risk management-as it has evolved over the past two decades-with an economists approach to the same issues, Financial Risk Management is the essential guide to the subject for todays complex world.
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  • Inhaltsangabe:Abstract: We discuss the main approaches to quantify the risk of losses arising from a defaulting counterparty to a financial transaction that have been developed over the last 25 years. Every existing method faces major problems in assessing the numerous and partly non-observable factors influencing credit risk. One shortcoming common to all methods is the classical normal assumption for interest rate changes and asset returns. Therefore we suggest the introduction of stable Paretian models to yield more realistic credit spreads. Inhaltsverzeichnis:Table of Contents: 1.Introduction 2.Basic Properties of Credit Risk Models 2.1Financial Position 2.2Default Probability 2.3The Price Of Credit Risk 3.Structural Models 3.1Structural Models With Constant Interest Rates 3.2Structural Models With Stochastic Interest Rates 4.Reduced Form Models 4.1Terminology of Reduced Form Models 4.1.1Credit Risk and Credit Events 4.1.2Rating Categories and Transition Matrices 4.2Reduced Form Modesl With Default Rates 4.3Reduced Form Models With Rating Transitions 4.3.1Modelling Rating Histories With Markov Chains 4.3.2The Introduction of Pseudo-Probabilities 4.3.3Parameter Estimation 5.Models With Implied Credit Spread 6.Hybrid Models 6.1Rating Transitions 6.2Forward Prices 6.3The Distribution of Values 6.3.1Distributions in Credit Risk and Market Risk Measurement 6.4Expected Loss 6.5Unexpected Loss 6.6Example 7.Rating Categories 7.1Alternative Credit Analysis And Rating Methodology 7...
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  • Lots of effort has been expended in improving volatility models since better forecasts translate in to better pricing of assets and better risk management. However the question as to what model should be used to calculate volatility, there is no unique answer as different volatility models were proposed in the literature and were being used by practitioners. To answer which VaR model adequately capture the market risk, three VaR models are tested on stock indices from Croatia, Serbia, Slovenia and Macedonia. The tested VaR models are: simple moving average with rolling windows of 50, 74 (proposed by Risk Metrics) and 100 days, EWMA using 0,9, 0,94 (proposed by Risk Metrics) and 0,96 as smoothing constant λ and different windows of 50, 74 and 100 days, and GARCH(1,1). VaR models are calculated for a one-day holding period at 95% and 99% coverage of the market risk. These competing models are evaluated on the basis of BLF error statistics. The challenge of this work is to come up with the best and easily implementable approach suitable to Former Yugoslavian stock exchange markets, especially for Macedonian and apply time series models for calculating Value at Risk.
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  • The aerospace community has long recognized and repeatedly emphasizes the importance of reliability for space systems. Despite this, little has been published in book form on the topic. Spacecraft Reliability and Multi-state Failures addresses this gap in the literature, offering a unique focus on spacecraft reliability based on extensive statistical analysis of system and subsystem anomalies and failures. The authors provide new results pertaining to spacecraft reliability based on extensive statistical analysis of on-orbit anomaly and failure data that will be particularly useful to spacecraft manufacturers and designers, for example in guiding satellite (and subsystem) test and screening programs and providing an empirical basis for subsystem redundancy and reliability growth plans. The authors develop nonparametric results and parametric models of spacecraft and spacecraft subsystem reliability and multi-state failures, quantify the relative contribution of each subsystem to the failure of the satellites thus identifying the subsystems that drive spacecraft unreliability, and propose advanced stochastic modeling and analysis tools for the reliability and survivability of spacecraft and space-based networks. Spacecraft Reliability and Multi-state Failures provides new nonparametric results pertaining to spacecraft reliability based on extensive statistical analysis of on-orbit anomaly and failure data; develops parametric models of spacecraft and spacecraft subsystem reliability and multi-state failures quantifies the relative contribution of each subsystem to the failure of the satellites proposes advanced stochastic modeling and analysis tools for the reliability and survivability of spacecraft and space-based networks. provides a dedicated treatment of the reliability and subsystem anomalies of communication spacecraft in geostationary orbit.
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  • The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management. Combining theory with practice, this book walks you through the fundamentals of credit risk management and shows you how to implement these concepts using the SAS credit risk management program, with helpful code provided. Coverage includes data analysis and preprocessing, credit scoring; PD and LGD estimation and forecasting, low default portfolios, correlation modeling and estimation, validation, implementation of prudential regulation, stress testing of existing modeling concepts, and more, to provide a one-stop tutorial and reference for credit risk analytics. The companion website offers examples of both real and simulated credit portfolio data to help you more easily implement the concepts discussed, and the expert author team provides practical insight on this real-world intersection of finance, statistics, and analytics. SAS is the preferred software for credit risk modeling due to its functionality and ability to process large amounts of data. This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate credit risk management models. Understand the general concepts of credit risk management Validate and stress-test existing models Access working examples based on both real and simulated data Learn useful code for implementing and validating models in SAS Despite the high demand for in-house models, there is little comprehensive training available; practitioners are left to comb through piece-meal resources, executive training courses, and consultancies to cobble together the information they need. This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process.
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  • This unique treatment systematically interprets a spectrum of importance measures to provide a comprehensive overview of their applications in the areas of reliability, network, risk, mathematical programming, and optimization. Investigating the precise relationships among various importance measures, it describes how they are modelled and combined with other design tools to allow users to solve readily many real-world, large-scale decision-making problems. Presenting the state-of-the-art in network analysis, multistate systems, and application in modern systems, this book offers a clear and complete introduction to the topic. Through describing the reliability importance and the fundamentals, it covers advanced topics such as signature of coherent systems, multi-linear functions, and new interpretation of the mathematical programming problems. Key highlights: Generalizes the concepts behind importance measures (such as sensitivity and perturbation analysis, uncertainty analysis, mathematical programming, network designs), enabling readers to address large-scale problems within various fields effectively Covers a large range of importance measures, including those in binary coherent systems, binary monotone systems, multistate systems, continuum systems, repairable systems, as well as importance measures of pairs and groups of components Demonstrates numerical and practical applications of importance measures and the related methodologies, including risk analysis in nuclear power plants, cloud computing, software reliability and more Provides thorough comparisons, examples and case studies on relations of different importance measures, with conclusive results based on the authors’ own research Describes reliability design such as redundancy allocation, system upgrading and component assignment. This book will benefit researchers and practitioners interested in systems design, reliability, risk and optimization, statistics, maintenance, prognostics and operations. Readers can develop feasible approaches to solving various open-ended problems in their research and practical work. Software developers, IT analysts and reliability and safety engineers in nuclear, telecommunications, offshore and civil industries will also find the book useful.
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  • An Integrated Approach to Product Development Reliability Engineering presents an integrated approach to the design, engineering, and management of reliability activities throughout the life cycle of a product, including concept, research and development, design, manufacturing, assembly, sales, and service. Containing illustrative guides that include worked problems, numerical examples, homework problems, a solutions manual, and class-tested materials, it demonstrates to product development and manufacturing professionals how to distribute key reliability practices throughout an organization. The authors explain how to integrate reliability methods and techniques in the Six Sigma process and Design for Six Sigma (DFSS). They also discuss relationships between warranty and reliability, as well as legal and liability issues. Other topics covered include: Reliability engineering in the 21st Century Probability life distributions for reliability analysis Process control and process capability Failure modes, mechanisms, and effects analysis Health monitoring and prognostics Reliability tests and reliability estimation Reliability Engineering provides a comprehensive list of references on the topics covered in each chapter. It is an invaluable resource for those interested in gaining fundamental knowledge of the practical aspects of reliability in design, manufacturing, and testing. In addition, it is useful for implementation and management of reliability programs.
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  • An introduction to risk assessment that utilizes key theory and state-of-the-art applications With its balanced coverage of theory and applications along with standards and regulations, Risk Assessment: Theory, Methods, and Applications serves as a comprehensive introduction to the topic. The book serves as a practical guide to current risk analysis and risk assessment, emphasizing the possibility of sudden, major accidents across various areas of practice from machinery and manufacturing processes to nuclear power plants and transportation systems. The author applies a uniform framework to the discussion of each method, setting forth clear objectives and descriptions, while also shedding light on applications, essential resources, and advantages and disadvantages. Following an introduction that provides an overview of risk assessment, the book is organized into two sections that outline key theory, methods, and applications. Introduction to Risk Assessment defines key concepts and details the steps of a thorough risk assessment along with the necessary quantitative risk measures. Chapters outline the overall risk assessment process, and a discussion of accident models and accident causation offers readers new insights into how and why accidents occur to help them make better assessments. Risk Assessment Methods and Applications carefully describes the most relevant methods for risk assessment, including preliminary hazard analysis, HAZOP, fault tree analysis, and event tree analysis. Here, each method is accompanied by a self-contained description as well as workflow diagrams and worksheets that illustrate the use of discussed techniques. Important problem areas in risk assessment, such as barriers and barrier analysis, human errors, and human reliability, are discussed along with uncertainty and sensitivity analysis. Each chapter concludes with a listing of resources for further study of the topic, and detailed appendices outline main results from probability and statistics, related formulas, and a listing of key terms used in risk assessment. A related website features problems that allow readers to test their comprehension of the presented material and supplemental slides to facilitate the learning process. Risk Assessment is an excellent book for courses on risk analysis and risk assessment at the upper-undergraduate and graduate levels. It also serves as a valuable reference for engineers, researchers, consultants, and practitioners who use risk assessment techniques in their everyday work.
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  • A classic book on credit risk management is updated to reflect the current economic crisis Credit Risk Management In and Out of the Financial Crisis dissects the 2007-2008 credit crisis and provides solutions for professionals looking to better manage risk through modeling and new technology. This book is a complete update to Credit Risk Measurement: New Approaches to Value at Risk and Other Paradigms, reflecting events stemming from the recent credit crisis. Authors Anthony Saunders and Linda Allen address everything from the implications of new regulations to how the new rules will change everyday activity in the finance industry. They also provide techniques for modeling-credit scoring, structural, and reduced form models-while offering sound advice for stress testing credit risk models and when to accept or reject loans. Breaks down the latest credit risk measurement and modeling techniques and simplifies many of the technical and analytical details surrounding them Concentrates on the underlying economics to objectively evaluate new models Includes new chapters on how to prevent another crisis from occurring Understanding credit risk measurement is now more important than ever. Credit Risk Management In and Out of the Financial Crisis will solidify your knowledge of this dynamic discipline.
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  • A one-stop guide for the theories, applications, and statistical methodologies essential to operational risk Providing a complete overview of operational risk modeling and relevant insurance analytics, Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk offers a systematic approach that covers the wide range of topics in this area. Written by a team of leading experts in the field, the handbook presents detailed coverage of the theories, applications, and models inherent in any discussion of the fundamentals of operational risk, with a primary focus on Basel II/III regulation, modeling dependence, estimation of risk models, and modeling the data elements. Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk begins with coverage on the four data elements used in operational risk framework as well as processing risk taxonomy. The book then goes further in-depth into the key topics in operational risk measurement and insurance, for example diverse methods to estimate frequency and severity models. Finally, the book ends with sections on specific topics, such as scenario analysis; multifactor modeling; and dependence modeling. A unique companion with Advances in Heavy Tailed Risk Modeling: A Handbook of Operational Risk, the handbook also features: Discussions on internal loss data and key risk indicators, which are both fundamental for developing a risk-sensitive framework Guidelines for how operational risk can be inserted into a firm’s strategic decisions A model for stress tests of operational risk under the United States Comprehensive Capital Analysis and Review (CCAR) program A valuable reference for financial engineers, quantitative analysts, risk managers, and large-scale consultancy groups advising banks on their internal systems, the handbook is also useful for academics teaching postgraduate courses on the methodology of operational risk.
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  • An essential resource for constructing and analyzing advanced actuarial models Loss Models: Further Topics presents extended coverage of modeling through the use of tools related to risk theory, loss distributions, and survival models. The book uses these methods to construct and evaluate actuarial models in the fields of insurance and business. Providing an advanced study of actuarial methods, the book features extended discussions of risk modeling and risk measures, including Tail-Value-at-Risk. Loss Models: Further Topics contains additional material to accompany the Fourth Edition of Loss Models: From Data to Decisions, such as: Extreme value distributions Coxian and related distributions Mixed Erlang distributions Computational and analytical methods for aggregate claim models Counting processes Compound distributions with time-dependent claim amounts Copula models Continuous time ruin models Interpolation and smoothing The book is an essential reference for practicing actuaries and actuarial researchers who want to go beyond the material required for actuarial qualification. Loss Models: Further Topics is also an excellent resource for graduate students in the actuarial field.
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  • Foundations of Risk Analysis presents the issues core to risk analysis – understanding what risk means, expressing risk, building risk models, addressing uncertainty, and applying probability models to real problems. The author provides the readers with the knowledge and basic thinking they require to successfully manage risk and uncertainty to support decision making. This updated edition reflects recent developments on risk and uncertainty concepts, representations and treatment. New material in Foundations of Risk Analysis includes: An up to date presentation of how to understand, define and describe risk based on research carried out in recent years. A new definition of the concept of vulnerability consistent with the understanding of risk. Reflections on the need for seeing beyond probabilities to measure/describe uncertainties. A presentation and discussion of a method for assessing the importance of assumptions (uncertainty factors) in the background knowledge that the subjective probabilities are based on A brief introduction to approaches that produce interval (imprecise) probabilities instead of exact probabilities. In addition the new version provides a number of other improvements, for example, concerning the use of cost-benefit analyses and the As Low As Reasonably Practicable (ALARP) principle. Foundations of Risk Analysis provides a framework for understanding, conducting and using risk analysis suitable for advanced undergraduates, graduates, analysts and researchers from statistics, engineering, finance, medicine and the physical sciences, as well as for managers facing decision making problems involving risk and uncertainty.
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  • Recent advances in science and technology have made modern computing and engineering systems more powerful and sophisticated than ever. The increasing complexity and scale imply that system reliability problems not only continue to be a challenge but also require more efficient models and solutions. This is the first book systematically covering the state-of-the-art binary decision diagrams and their extended models, which can provide efficient and exact solutions to reliability analysis of large and complex systems. The book provides both basic concepts and detailed algorithms for modelling and evaluating reliability of a wide range of complex systems, such as multi-state systems, phased-mission systems, fault-tolerant systems with imperfect fault coverage, systems with common-cause failures, systems with disjoint failures, and systems with functional dependent failures. These types of systems abound in safety-critical or mission-critical applications such as aerospace, circuits, power systems, medical systems, telecommunication systems, transmission systems, traffic light systems, data storage systems, and etc. The book provides both small-scale illustrative examples and large-scale benchmark examples to demonstrate broad applications and advantages of different decision diagrams based methods for complex system reliability analysis. Other measures including component importance and failure frequency are also covered. A rich set of references is cited in the book, providing helpful resources for readers to pursue further research and study of the topics. The target audience of the book is reliability and safety engineers or researchers. The book can serve as a textbook on system reliability analysis. It can also serve as a tutorial and reference book on decision diagrams, multi-state systems, phased-mission systems, and imperfect fault coverage models.
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  • How to apply operational risk theory to real-life banking data Modelling Operational and Reputational Risks shows practitioners the best models to use in a given situation, according to the type of risk an organization is facing. Based on extensive applied research on operational risk models using real bank datasets, it offers a wide range of various testing models and fitting techniques for financial practitioners. With this book, professionals will have a foundation for measuring and predicting these important intangibles. Aldo Soprano (Madrid, Spain) is Group Head of operational risk management at UniCredit Group.
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  • Optimal Portfolio Modeling is an easily accessible introduction to portfolio modeling for those who prefer an intuitive approach to this discipline. While early chapters provide engaging insights on the statistical properties of markets, this book quickly moves on to illustrate invaluable trading and risk control models based on popular programs such as Excel and the statistical modeling language R. This reliable resource presents modeling formulas that will allow you to effectively maximize the performance, minimize the drawdown, and manage the risk of your portfolio.
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