Capital Structure Decisions. Evaluating Risk and Uncertainty

A comprehensive guide to making better capital structure and corporate financing decisions in today's dynamic business environment Given the dramatic changes that have recently occurred in the economy, the topic of capital structure and corporate financing decisions is critically important. The fact is that firms need to constantly revisit their portfolio of debt, equity, and hybrid securities to finance assets, operations, and future growth. Capital Structure and Corporate Financing Decisions provides an in-depth examination of critical capital structure topics, including discussions of basic capital structure components, key theories and practices, and practical application in an increasingly complex corporate world. Throughout, the book emphasizes how a sound capital structure simultaneously minimizes the firm's cost of capital and maximizes the value to shareholders. Offers a strategic focus that allows you to understand how financing decisions relates to a firm's overall corporate policy Consists of contributed chapters from both academics and experienced professionals, offering a variety of perspectives and a rich interplay of ideas Contains information from survey research describing actual financial practices of firms This valuable resource takes a practical approach to capital structure by discussing why various theories make sense and how firms use them to solve problems and create wealth. In the wake of the recent financial crisis, the insights found here are essential to excelling in today's volatile business environment.

Written by leading experts in the field, Social Theories of Risk and Uncertainty is an introduction to mainstream theorizing on risk and uncertainty in sociology. Provides an overview of the historical developments and conceptual aspects of risk Identifies why theorizing on risk is necessary and highlights specific sociological contributions to this field of research Explores key topics including risk society and reflexive modernization, culture and risk, governmentality and risk, systems theory and risk, and edgework and voluntary risk taking Offers a comprehensive look at the promises, pitfalls, and perspectives of risk theorizing

Explores methods for the representation and treatment of uncertainty in risk assessment In providing guidance for practical decision-making situations concerning high-consequence technologies (e.g., nuclear, oil and gas, transport, etc.), the theories and methods studied in Uncertainty in Risk Assessment have wide-ranging applications from engineering and medicine to environmental impacts and natural disasters, security, and financial risk management. The main focus, however, is on engineering applications. While requiring some fundamental background in risk assessment, as well as a basic knowledge of probability theory and statistics, Uncertainty in Risk Assessment can be read profitably by a broad audience of professionals in the field, including researchers and graduate students on courses within risk analysis, statistics, engineering, and the physical sciences. Uncertainty in Risk Assessment: Illustrates the need for seeing beyond probability to represent uncertainties in risk assessment contexts. Provides simple explanations (supported by straightforward numerical examples) of the meaning of different types of probabilities, including interval probabilities, and the fundamentals of possibility theory and evidence theory. Offers guidance on when to use probability and when to use an alternative representation of uncertainty. Presents and discusses methods for the representation and characterization of uncertainty in risk assessment. Uses examples to clearly illustrate ideas and concepts.

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 training Analysts and researchers in numerical modeling, applied statistics, scientific computing, reliability, advanced engineering, natural risk or environmental science will benefit from this book.

Essential guidance for the corporate finance professional – advisor, Board Director, CFO, Treasurer, business development executive, or M&A expert—to ask the right questions and make the critical decisions. Strategic Corporate Finance is a practical guide to the key issues, their context, and their solutions. From performance measurement and capital planning to risk management and capital structure, Strategic Corporate Finance, translates principles of corporate finance theory into practical methods for implementing them. Filled with in-depth insights, expert advice, and detailed case studies, Strategic Corporate Finance will prepare you for the issues involved in raising, allocating and managing capital, and its associated risks. Justin Pettit (New York, NY) draws on his 15 years of senior advisory experience as an investment banker and management consultant. He advises corporate boards and executives on matters of capital structure, financial policy, valuation, and strategy. He also lectures on topics in advanced corporate finance to graduate and undergraduate students at universities in the New York area.

Deal with information and uncertainty properly and efficiently using tools emerging from generalized information theory Uncertainty and Information: Foundations of Generalized Information Theory contains comprehensive and up-to-date coverage of results that have emerged from a research program begun by the author in the early 1990s under the name «generalized information theory» (GIT). This ongoing research program aims to develop a formal mathematical treatment of the interrelated concepts of uncertainty and information in all their varieties. In GIT, as in classical information theory, uncertainty (predictive, retrodictive, diagnostic, prescriptive, and the like) is viewed as a manifestation of information deficiency, while information is viewed as anything capable of reducing the uncertainty. A broad conceptual framework for GIT is obtained by expanding the formalized language of classical set theory to include more expressive formalized languages based on fuzzy sets of various types, and by expanding classical theory of additive measures to include more expressive non-additive measures of various types. This landmark book examines each of several theories for dealing with particular types of uncertainty at the following four levels: * Mathematical formalization of the conceived type of uncertainty * Calculus for manipulating this particular type of uncertainty * Justifiable ways of measuring the amount of uncertainty in any situation formalizable in the theory * Methodological aspects of the theory With extensive use of examples and illustrations to clarify complex material and demonstrate practical applications, generous historical and bibliographical notes, end-of-chapter exercises to test readers' newfound knowledge, glossaries, and an Instructor's Manual, this is an excellent graduate-level textbook, as well as an outstanding reference for researchers and practitioners who deal with the various problems involving uncertainty and information. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Science is a quest for certainty, but lack of certainty is the driving force behind all of its endeavors. This book, specifically, examines the uncertainty of technological and industrial science. Uncertainty and Mechanics studies the concepts of mechanical design in an uncertain setting and explains engineering techniques for inventing cost-effective products. Though it references practical applications, this is a book about ideas and potential advances in mechanical science.

A detailed, expert-driven guide to today's major financial point of interest The xVA Challenge: Counterparty Credit Risk, Funding, Collateral, and Capital is a practical guide from one of the leading and most influential credit practitioners, Jon Gregory. Focusing on practical methods, this informative guide includes discussion around the latest regulatory requirements, market practice, and academic thinking. Beginning with a look at the emergence of counterparty risk during the recent global financial crisis, the discussion delves into the quantification of firm-wide credit exposure and risk mitigation methods, such as netting and collateral. It also discusses thoroughly the xVA terms, notably CVA, DVA, FVA, ColVA, and KVA and their interactions and overlaps. The discussion of other aspects such as wrong-way risks, hedging, stress testing, and xVA management within a financial institution are covered. The extensive coverage and detailed treatment of what has become an urgent topic makes this book an invaluable reference for any practitioner, policy maker, or student. Counterparty credit risk and related aspects such as funding, collateral, and capital have become key issues in recent years, now generally characterized by the term 'xVA'. This book provides practical, in-depth guidance toward all aspects of xVA management. Market practice around counterparty credit risk and credit and debit value adjustment (CVA and DVA) The latest regulatory developments including Basel III capital requirements, central clearing, and mandatory collateral requirements The impact of accounting requirements such as IFRS 13 Recent thinking on the applications of funding, collateral, and capital adjustments (FVA, ColVA and KVA) The sudden realization of extensive counterparty risks has severely compromised the health of global financial markets. It's now a major point of action for all financial institutions, which have realized the growing importance of consistent treatment of collateral, funding, and capital alongside counterparty risk. The xVA Challenge: Counterparty Credit Risk, Funding, Collateral, and Capital provides expert perspective and real-world guidance for today's institutions.

Since I wrote the Foreword for the second edition of this book, risk management processes have become much more widely used, but controversy about what should be done and how best to do it has grown. Managing risk is a risky business. Chapman and Ward provide an in-depth explanation of why it is important to understand and manage underlying uncertainty in all its forms, in order to realise opportunities more fully and enhance corporate performance. They show what best practice should look like. The implications go well beyond the conventional wisdom of project risk management, providing an enlightening new perspective. —Professor Tony M. Ridley Imperial College London, Past President, Institution of Civil Engineers Chris Chapman and Stephen Ward continue to educate the profession with this masterful exposition of the differences between, and the potentials for combinations of, risk, uncertainty and opportunity. Particularly welcome is the way they integrate this trio into the project lifecycle – the bedrock of project management control and organization. —Peter W.G. Morris Head of School and Professor of Construction and Project Management University College London Chris Chapman and Stephen Ward’s books on Project Risk Management have been an essential part of my repertoire for twenty years, and they are top of my recommended reading for the courses I do on that subject. In this book they have enhanced their previous work to focus on uncertainty management and emphasise more strongly opportunities for improving project performance, rather then just identifying what can go wrong. A structured process is an essential part of managing project uncertainty, and their process is one of the most powerful. This book will be added to my repertoire. —Rodney Turner Professor of Project Management, SKEMA Business School Lille A profoundly important book. With How to Manage Project Opportunity and Risk, Chris Chapman and Stephen Ward take a good thing and make it better. Members of the project management profession have been influenced for years by their insights into project risk management. With this latest instalment the authors demonstrate that risk and uncertainty needn’t be dreaded; in fact, the reverse side of the ‘risk coin’ has always been opportunity. My sincere appreciation to Chapman and Ward for turning this particular coin over and showing readers, academic and practitioner alike, the opportunity embedded in managing projects. —Jeffrey K. Pinto Andrew Morrow and Elizabeth Lee Black Chair in Management of Technology Sam and Irene Black School of Business, Penn State Erie

The first part of the book defines the concept of uncertainties and the mathematical frameworks that will be used for uncertainty modeling. The application to system reliability assessment illustrates the concept. In the second part, evidential networks as a new tool to model uncertainty in reliability and risk analysis is proposed and described. Then it is applied on SIS performance assessment and in risk analysis of a heat sink. In the third part, Bayesian and evidential networks are used to deal with important measures evaluation in the context of uncertainties.