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Financial markets respond to information virtually instantaneously. Each new piece of information influences the prices of assets and their correlations with each other, and as the system rapidly changes, so too do correlation forecasts. This fast-evolving environment presents econometricians with the challenge of forecasting dynamic correlations, which are essential inputs to risk measurement, portfolio allocation, derivative pricing, and many other critical financial activities. In Anticipating Correlations, Nobel Prize-winning economist Robert Engle introduces an important new method for estimating correlations for large systems of assets: Dynamic Conditional Correlation (DCC). Engle demonstrates the role of correlations in financial decision making, and addresses the economic underpinnings and theoretical properties of correlations and their relation to other measures of dependence. He compares DCC with other correlation estimators such as historical correlation, exponential smoothing, and multivariate GARCH, and he presents a range of important applications of DCC. Engle presents the asymmetric model and illustrates it using a multicountry equity and bond return model. He introduces the new FACTOR DCC model that blends factor models with the DCC to produce a model with the best features of both, and illustrates it using an array of U.S. large-cap equities. Engle shows how overinvestment in collateralized debt obligations, or CDOs, lies at the heart of the subprime mortgage crisis--and how the correlation models in this book could have foreseen the risks. A technical chapter of econometric results also is included.

Modelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated:How uncertain is my model ? Is it truly valuable to support decision-making ? What kind of decision can be truly supported and how can I handle residual uncertainty ? How much refined should the mathematical description be, given the true data limitations ? Could the uncertainty be reduced through more data, increased modeling investment or computational budget ? Should it be reduced now or later ? How robust is the analysis or the computational methods involved ? Should / could those methods be more robust ? Does it make sense to handle uncertainty, risk, lack of knowledge, variability or errors altogether ? How reasonable is the choice of probabilistic modeling for rare events ? How rare are the events to be considered ? How far does it make sense to handle extreme events and elaborate confidence figures ? Can I take advantage of expert / phenomenological knowledge to tighten the probabilistic figures ? Are there connex domains that could provide models or inspiration for my problem ?Written by a leader at the crossroads of industry, academia and engineering, and based on decades of multi-disciplinary field experience, Modelling Under Risk and Uncertainty gives a self-consistent introduction to the methods involved by any type of modeling development acknowledging the inevitable uncertainty and associated risks. It goes beyond the \"black-box\" view that some analysts, modelers, risk experts or statisticians develop on the underlying phenomenology of the environmental or industrial processes, without valuing enough their physical properties and inner modelling potential nor challenging the practical plausibility of mathematical hypotheses; conversely it is also to attract environmental or engineering modellers to better handle model confidence issues through finer statistical and risk analysis material taking advantage of advanced scientific computing, to face new regulations departing from deterministic design or support robust decision-making.Modelling Under Risk and Uncertainty:Addresses a concern of growing interest for large industries, environmentalists or analysts: robust modeling for decision-making in complex systems.Gives new insights into the peculiar mathematical and computational challenges generated by recent industrial safety or environmental control analysis for rare events.Implements decision theory choices differentiating or aggregating the dimensions of risk/aleatory and epistemic uncertainty through a consistent multi-disciplinary set of statistical estimation, physical modelling, robust computation and risk analysis.Provides an original review of the advanced inverse probabilistic approaches for model identification, calibration or data assimilation, key to digest fast-growing multi-physical data acquisition.Illustrated with one favourite pedagogical example crossing natural risk, engineering and economics, developed throughout the book to facilitate the reading and understanding.Supports Master/PhD-level course as well as advanced tutorials for professional trainingAnalysts and researchers in numerical modeling, applied statistics, scientific computing, reliability, advanced engineering, natural risk or environmental science will benefit from this book.

Mathematics and Statistics for Financial Risk Management is a practical guide to modern financial risk management for both practitioners and academics. Now in its second edition with more topics, more sample problems and more real world examples, this popular guide to financial risk management introduces readers to practical quantitative techniques for analyzing and managing financial risk. In a concise and easy-to-read style, each chapter introduces a different topic in mathematics or statistics. As different techniques are introduced, sample problems and application sections demonstrate how these techniques can be applied to actual risk management problems. Exercises at the end of each chapter and the accompanying solutions at the end of the book allow readers to practice the techniques they are learning and monitor their progress. A companion Web site includes interactive Excel spreadsheet examples and templates. Mathematics and Statistics for Financial Risk Management is an indispensable reference for today's financial risk professional.

This title describes 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.

The general perception amongst most project and risk managers that we can somehow control the future is, says David Hancock, one of the most ill-conceived in risk management. The biggest problem is how to measure risks in terms of their potential likelihood, their possible consequences, their correlation and the public's perception of them. The situation is further complicated by identifying different categories of problem types; Tame problems (straight-forward simple linear causal relationships and can be solved by analytical methods), and 'messes' which have high levels of system complexity and have interrelated or interdependent problems needing to be considered holistically. However, when an overriding social theory or social ethic is not shared the project or risk manager also faces 'wickedness'. Wicked problems are characterised by high levels of behavioural complexity, but what confuses real decision-making is that behavioural and dynamic complexities co-exist and interact in what is known as wicked messes. Tame, Messy and Wicked Risk Leadership will help professionals understand the limitations of the present project and risk management techniques. It introduces the concepts of societal benefit and behavioural risk, and illustrates why project risk has followed a particular path, developing from the basis of engineering, science and mathematics. David Hancock argues for, and offers, complimentary models from the worlds of sociology, philosophy and politics to be added to the risk toolbox, and provides a framework to understand which particular type of problem (tame, messy, wicked or messy and wicked) may confront you and which tools will provide the greatest potential for successful outcomes. Finally he introduces the concept of 'risk leadership' to aid the professional in delivering projects in a world of uncertainty and ambiguity. Anyone who has experienced the pain and blame of projects faced with overruns of time or money, dissatisfied stakeholders or basic failure, will welcome this imaginative reframing of some aspects of risk management. This is a book that has implications for the risk management processes, culture, and outcomes, of large and complex projects of all kinds. Contents: Preface; Part 1 The Basis for Current Project Risk Methodologies: Introduction; Risk and risk management. Part 2 The Tame, Messy and Wicked Model: Problem types and systems complexity; Problem types and behavioural complexity. Part 3 Strategies for Wicked and Messy Environments: Risk therapy - the talking cure?; Conclusion; Index. Dr David Hancock is Head of Construction for the UK Cabinet Office. Previously he was Head of Project Risk for London Underground, part of Transport for London. He has run his own consultancy, and was Director of Risk and Assurance for the London Development Agency (LDA) - under both Ken Livingstone and Boris Johnson's leadership - with responsibilities for risk management activities and audit for all of the Agency's and its partner's programmes. Prior to this for 6 years he was Executive Director with the Halcrow Group, responsible for establishing and expanding the business consultancy group. He has a wide breadth of knowledge in project management and complex projects developed over more than 20 years and extensive experience in opportunity and risk management, with special regard to people and behavioural aspects. He is a board director with Alarm (The National Forum for Risk Management in the Public Sector), a co-director of the managing partners' forum risk panel, member of the programme committee for the Major Projects Association and a visiting Fellow at Cranfield University in their School of Management.

Top businesses recognise risk management as a core feature of their project management process and approach to the governance of projects. However, a mature risk management process is required in order to realise its benefits; one that takes into account the design and implementation of the process and the skills, experience and culture of the people who use it. To be mature in the way you manage risk you need an accepted framework to assess your risk management maturity, allowing you to benchmark against a recognised standard. A structured pathway for improvement is also needed, not just telling you where you are now, but describing the steps required to reach the next level. The Project Risk Maturity Model detailed here provides such an assessment framework and development pathway. It can be used to benchmark your project risk processes and support the introduction of effective in-house project risk management. Using this model, implementation and improvement of project risk management can be managed effectively to ensure that the expected benefits are achieved in a way that is appropriate to the needs of each organisation. Martin Hopkinson has developed The Project Risk Maturity Model into a robust framework, and this book allows you to access and apply his insights and experience. A key feature is a CD containing a working copy of the QinetiQ Project Risk Maturity Model (RMM). This will enable you to undertake maturity assessments for as many projects as you choose. The RMM has been proven over a period of 10 years, with at least 250 maturity assessments on projects and programmes with a total value exceeding £60 billion. A case study in the book demonstrates how it has been used to deliver significant and measurable benefits to the performance of major projects. Martin Hopkinson APMP is a Principal Consultant with QinetiQ, specialising in risk management and project governance. Martin has led the risk management process on several multi-billion pound projects and is lead developer for the Risk Maturity Model. Martin is co-author of the Association for Project Management's Project Risk Analysis and Management (PRAM) Guide and led the group that produced the APM's guide Prioritising Project Risks. As a member of the APM's Governance of Project Management SIG, he was on the editing committee for the APM's guide Directing Change. On behalf of the SIG he sponsored Co-Directing Change - a guide to the governance of multi-owned projects and Sponsoring Change - a guide to the governance aspects of project sponsorship. Contents: Foreword; Preface; Part I Introduction to the Project Risk Maturity Model: The project risk maturity model; Scope and context; Starting from the top: using a multi-pass risk management process; The UK MoD defence procurement agency: a project risk maturity model case study; Risk maturity model data collection; Part II Guide to the Project Risk Maturity Model: Stakeholders; Risk identification; Risk analysis; Risk reponses; Project management; Risk management culture; Appendices; References; Software user instructions; Index.