Engineering Risk And Finance

Risk control, capital allocation, and realistic derivative pricing and hedging are critical concerns for major financial institutions and individual traders alike. Events from the collapse of Lehman Brothers to the Greek sovereign debt crisis demonstrate the urgent and abiding need for statistical tools adequate to measure and anticipate the amplitude of potential swings in the financial markets—from ordinary stock price and interest rate moves, to defaults, to those increasingly frequent "rare events" fashionably called black swan events. Yet many on Wall Street continue to rely on standard models based on artificially simplified assumptions that can lead to systematic (and sometimes catastrophic) underestimation of real risks. In Practical Methods of Financial Engineering and Risk Management, Dr. Rupak Chatterjee— former director of the multi-asset quantitative research group at Citi—introduces finance professionals and advanced students to the latest concepts, tools, valuation techniques, and analytic measures being deployed by the more discerning and responsive Wall Street practitioners, on all operational scales from day trading to institutional strategy, to model and analyze more faithfully the real behavior and risk exposure of financial markets in the cold light of the post-2008 realities. Until one masters this modern skill set, one cannot allocate risk capital properly, price and hedge derivative securities realistically, or risk-manage positions from the multiple perspectives of market risk, credit risk, counterparty risk, and systemic risk. The book assumes a working knowledge of calculus, statistics, and Excel, but it teaches techniques from statistical analysis, probability, and stochastic processes sufficient to enable the reader to calibrate probability distributions and create the simulations that are used on Wall Street to valuate various financial instruments correctly, model the risk dimensions of trading strategies, and perform the numerically intensive analysis of risk measures required by various regulatory agencies.

Risk models are models of uncertainty, engineered for some purposes. They are “educated guesses and hypotheses” assessed and valued in terms of well-defined future states and their consequences. They are engineered to predict, to manage countable and accountable futures and to provide a frame of reference within which we may believe that “uncertainty is tamed”. Quantitative-statistical tools are used to reconcile our information, experience and other knowledge with hypotheses that both serve as the foundation of risk models and also value and price risk. Risk models are therefore common to most professions, each with its own methods and techniques based on their needs, experience and a wisdom accrued over long periods of time. This book provides a broad and interdisciplinary foundation to engineering risks and to their financial valuation and pricing. Risk models applied in industry and business, heath care, safety, the environment and regulation are used to highlight their variety while financial valuation techniques are used to assess their financial consequences. This book is technically accessible to all readers and students with a basic background in probability and statistics (with 3 chapters devoted to introduce their elements). Principles of risk measurement, valuation and financial pricing as well as the economics of uncertainty are outlined in 5 chapters with numerous examples and applications. New results, extending classical models such as the CCAPM are presented providing insights to assess the risks and their price in an interconnected, dependent and strategic economic environment. In an environment departing from the fundamental assumptions we make regarding financial markets, the book provides a strategic/game-like approach to assess the risk and the opportunities that such an environment implies. To control these risks, a strategic-control approach is developed that recognizes that many risks resulting by “what we do” as well as “what others do”. In particular we address the strategic and statistical control of compliance in large financial institutions confronted increasingly with a complex and far more extensive regulation.

This text provides a thorough treatment of futures, 'plain vanilla' options and swaps as well as the use of exotic derivatives and interest rate options for speculation and hedging. Pricing of options using numerical methods such as lattices (BOPM), Mone Carlo simulation and finite difference methods, in additon to solutions using continuous time mathematics, are also covered. Real options theory and its use in investment appraisal and in valuing internet and biotechnology companies provide cutting edge practical applications. Practical risk management issues are examined in depth. Alternative models for calculating Value at Risk (market risk) and credit risk provide the throretical basis for a practical and timely overview of these areas of regulatory policy. This book is designed for courses in derivatives and risk management taken by specialist MBA, MSc Finance students or final year undergraduates, either as a stand-alone text or as a follow-on to Investments: Spot and Derivatives Markets by the same authors. The authors adopt a real-world emphasis throughout, and include features such as: * topic boxes, worked examples and learning objectives * Financial Times and Wall Street Journal newspaper extracts and analysis of real world cases * supporting web site including Lecturer's Resource Pack and Student Centre with interactive Excel and GAUSS software

Risk models are models of uncertainty, engineered for some purposes. They are “educated guesses and hypotheses” assessed and valued in terms of well-defined future states and their consequences. They are engineered to predict, to manage countable and accountable futures and to provide a frame of reference within which we may believe that “uncertainty is tamed”. Quantitative-statistical tools are used to reconcile our information, experience and other knowledge with hypotheses that both serve as the foundation of risk models and also value and price risk. Risk models are therefore common to most professions, each with its own methods and techniques based on their needs, experience and a wisdom accrued over long periods of time. This book provides a broad and interdisciplinary foundation to engineering risks and to their financial valuation and pricing. Risk models applied in industry and business, heath care, safety, the environment and regulation are used to highlight their variety while financial valuation techniques are used to assess their financial consequences. This book is technically accessible to all readers and students with a basic background in probability and statistics (with 3 chapters devoted to introduce their elements). Principles of risk measurement, valuation and financial pricing as well as the economics of uncertainty are outlined in 5 chapters with numerous examples and applications. New results, extending classical models such as the CCAPM are presented providing insights to assess the risks and their price in an interconnected, dependent and strategic economic environment. In an environment departing from the fundamental assumptions we make regarding financial markets, the book provides a strategic/game-like approach to assess the risk and the opportunities that such an environment implies. To control these risks, a strategic-control approach is developed that recognizes that many risks resulting by “what we do” as well as “what others do”. In particular we address the strategic and statistical control of compliance in large financial institutions confronted increasingly with a complex and far more extensive regulation.

Financial Engineering is a text with a methodological thread, making it appropriate as a reference text. Risk management and measure and control of volatility is a major theme, but broader financial issues are also covered to provide the reader with a conceptual framework to manipulate and evaluate financial instruments. Errington's text analyses the spectrum of financial engineering including explanations of financial axioms and mathematical techniques with a summary of the instruments and worked examples of how they operate. As well as risk management, arbitrageurs are also catered for, to show how instruments can be valued, deconstructed and repackaged.

Financial engineering is about using financial instruments to reduce or eliminate risk, or to restructure financial exposure to improve its characteristics. Written with a clear and concise style, it covers the tools of financial engineering, defines each instrument, describes the markets in which they are traded and explains how each product is priced and hedged.

More than any other book available, Risk Analysis in Engineering and Economics introduces the fundamental concepts, techniques, and applications of the subject in a style tailored to meet the needs of students and practitioners of engineering, science, economics, and finance. Drawing on his extensive experience in uncertainty and risk modeling and analysis, the author leads readers from the fundamental concepts through the theory, applications, and data requirements, sources, and collection. He emphasizes the practical use of the methods presented and carefully examines the limitations, advantages, and disadvantages of each. Case studies that incorporate the techniques discussed offer a practical perspective that helps readers clearly identify and solve problems encountered in practice. If you deal with decision-making under conditions of uncertainty, this book is required reading. The presentation includes more than 300 tables and figures, more than 100 examples, many case studies, and a wealth of end-of-chapter problems. Unlike the classical books on reliability and risk assessment, this book helps you relate underlying concepts to everyday applications and better prepares you to understand and use the methods of risk analysis.

The financial systems in most developed countries today build up a large amount of model risk on a daily basis. However, this is not particularly visible as the financial risk management agenda is still dominated by the subprime-liquidity crisis, the sovereign crises, and other major political events. Losses caused by model risk are hard to identify and even when they are internally identified, as such, they are most likely to be classified as normal losses due to market evolution. Model Risk in Financial Markets: From Financial Engineering to Risk Management seeks to change the current perspective on model innovation, implementation and validation. This book presents a wide perspective on model risk related to financial markets, running the gamut from financial engineering to risk management, from financial mathematics to financial statistics. It combines theory and practice, both the classical and modern concepts being introduced for financial modelling. Quantitative finance is a relatively new area of research and much has been written on various directions of research and industry applications. In this book the reader gradually learns to develop a critical view on the fundamental theories and new models being proposed. Contents:IntroductionFundamental RelationshipsModel Risk in Interest Rate ModellingArbitrage TheoryDerivatives Pricing Under UncertaintyPortfolio Selection Under UncertaintyProbability Pitfalls of Financial CalculusModel Risk in Risk Measures CalculationsParameter Estimation RiskComputational ProblemsPortfolio Selection Using Sharpe RatioBayesian Calibration for Low Frequency DataMCMC Estimation of Credit Risk MeasuresLast But Not Least. Can We Avoid the Next Big Systemic Financial Crisis?Notations for the Study of MLE for CIR Process Readership: Graduate students, researchers, practitioners, senior managers in financial institutions and hedge-funds, regulators and risk managers, who are keen to understand the pitfalls of financial modelling, and also those who are looking for a career in model validation, product control and risk management functions. Key Features:Some innovative results are presented for the first timeCovers a wide range of models, results and applications in financial markets to demonstrate that model risk is generally spreadKeywords:Model Risk;Risk Management;Financial Engineering;Financial Markets