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Risk and Financial Management,Mathematical and Computational Methods
Financial risk management has become a popular practice amongstfinancial institutions to protect against the adverse effects ofuncertainty caused by fluctuations in interest rates, exchangerates, commodity prices, and equity prices. New financialinstruments and mathematical techniques are continuously developedand introduced in financial practice. These techniques are beingused by an increasing number of firms, traders and financial riskmanagers across various industries. Risk and FinancialManagement: Mathematical and Computational Methods confrontsthe many issues and controversies, and explains the fundamentalconcepts that underpin financial risk management. Provides a comprehensive introduction to the core topics ofrisk and financial management. Adopts a pragmatic approach, focused on computational, ratherthan just theoretical, methods. Bridges the gap between theory and practice in financial riskmanagement Includes coverage of utility theory, probability, options andderivatives, stochastic volatility and value at risk. Suitable for students of risk, mathematical finance, andfinancial risk management, and finance practitioners. Includes extensive reference lists, applications andsuggestions for further reading. Risk and Financial Management: Mathematical and ComputationalMethods is ideally suited to both students of mathematicalfinance with little background in economics and finance, andstudents of financial risk management, as well as financepractitioners requiring a clearer understanding of the mathematicaland computational methods they use every day. It combines therequired level of rigor, to support the theoretical developments,with a practical flavour through many examples andapplications.
by Charles S. Tapiero
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Computational Methods and Experimental Measurements XVII,
Containing papers presented at the seventeenth in a series of biennial meetings organised by the Wessex Institute and first held in 1984, this book includes the latest research from scientists who perform experiments, researchers who develop computer codes, and those who carry out measurements on prototypes and whose work may interact. Progress in the engineering sciences is dependent on the orderly and concurrent development of all three fields. Continuous improvement in computer efficiency, coupled with diminishing costs and rapid development of numerical procedures have generated an ever-increasing expansion of computational simulations that permeate all fields of science and technology. As these procedures continue to grow in magnitude and complexity, it is essential to be certain of their reliability, i.e. to validate their results. This can be achieved by performing dedicated and accurate experiments. At the same time, current experimental techniques have become more complex and sophisticated so that they require the exploitation of computers, both for running experiments as well as acquiring and processing the resulting data. The papers contained in the book address advances in the interaction between these three areas. They cover such topics as: Computational and Experimental Methods; Fluid Flow; Structural and Stress Analysis; Materials Characterisation; Heat Transfer and Thermal Processes; Advances in Computational Methods; Automotive Applications; Applications in Industry; Process Simulations; Environmental Modelling and Applications; Computer Modelling; Validation of Computer Modelling; Computation in Measurements; Data Processing of Experiments; Virtual Testing and Verification; Simulation and Forecasting; Measurements in Engineering.
by G.M. Carlomagno
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Computational Methods for Reliability and Risk Analysis,
This book illustrates a number of modelling and computational techniques for addressing relevant issues in reliability and risk analysis. In particular, it provides: i) a basic illustration of some methods used in reliability and risk analysis for modelling the stochastic failure and repair behaviour of systems, e.g. the Markov and Monte Carlo simulation methods; ii) an introduction to Genetic Algorithms, tailored to their application for RAMS (Reliability, Availability, Maintainability and Safety) optimization; iii) an introduction to key issues of system reliability and risk analysis, like dependent failures and importance measures; and iv) a presentation of the issue of uncertainty and of the techniques of sensitivity and uncertainty analysis used in support of reliability and risk analysis. The book provides a technical basis for senior undergraduate or graduate courses and a reference for researchers and practitioners in the field of reliability and risk analysis. Several practical examples are included to demonstrate the application of the concepts and techniques in practice.
by Enrico Zio
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Computational Methods in Financial Engineering,Essays in Honour of Manfred Gilli
Computational models and methods are central to the analysis of economic and financial decisions. Simulation and optimisation are widely used as tools of analysis, modelling and testing. The focus of this book is the development of computational methods and analytical models in financial engineering that rely on computation. The book contains eighteen chapters written by leading researchers in the area on portfolio optimization and option pricing; estimation and classification; banking; risk and macroeconomic modelling. It explores and brings together current research tools and will be of interest to researchers, analysts and practitioners in policy and investment decisions in economics and finance.
by Erricos Kontoghiorghes
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Advances in Queueing Theory, Methods, and Open Problems,
The progress of science and technology has placed Queueing Theory among the most popular disciplines in applied mathematics, operations research, and engineering. Although queueing has been on the scientific market since the beginning of this century, it is still rapidly expanding by capturing new areas in technology. Advances in Queueing provides a comprehensive overview of problems in this enormous area of science and focuses on the most significant methods recently developed. Written by a team of 24 eminent scientists, the book examines stochastic, analytic, and generic methods such as approximations, estimates and bounds, and simulation. The first chapter presents an overview of classical queueing methods from the birth of queues to the seventies. It also contains the most comprehensive bibliography of books on queueing and telecommunications to date. Each of the following chapters surveys recent methods applied to classes of queueing systems and networks followed by a discussion of open problems and future research directions. Advances in Queueing is a practical reference that allows the reader quick access to the latest methods.
by Jewgeni H. Dshalalow
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Computational Actuarial Science with R,
A Hands-On Approach to Understanding and Using Actuarial Models Computational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes. After an introduction to the R language, the book is divided into four parts. The first one addresses methodology and statistical modeling issues. The second part discusses the computational facets of life insurance, including life contingencies calculations and prospective life tables. Focusing on finance from an actuarial perspective, the next part presents techniques for modeling stock prices, nonlinear time series, yield curves, interest rates, and portfolio optimization. The last part explains how to use R to deal with computational issues of nonlife insurance. Taking a do-it-yourself approach to understanding algorithms, this book demystifies the computational aspects of actuarial science. It shows that even complex computations can usually be done without too much trouble. Datasets used in the text are available in an R package (CASdatasets).
by Arthur Charpentier
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Computational Methods in Finance,
As today’s financial products have become more complex, quantitative analysts, financial engineers, and others in the financial industry now require robust techniques for numerical analysis. Covering advanced quantitative techniques, Computational Methods in Finance explains how to solve complex functional equations through numerical methods. The first part of the book describes pricing methods for numerous derivatives under a variety of models. The book reviews common processes for modeling assets in different markets. It then examines many computational approaches for pricing derivatives. These include transform techniques, such as the fast Fourier transform, the fractional fast Fourier transform, the Fourier-cosine method, and saddlepoint method; the finite difference method for solving PDEs in the diffusion framework and PIDEs in the pure jump framework; and Monte Carlo simulation. The next part focuses on essential steps in real-world derivative pricing. The author discusses how to calibrate model parameters so that model prices are compatible with market prices. He also covers various filtering techniques and their implementations and gives examples of filtering and parameter estimation. Developed from the author’s courses at Columbia University and the Courant Institute of New York University, this self-contained text is designed for graduate students in financial engineering and mathematical finance as well as practitioners in the financial industry. It will help readers accurately price a vast array of derivatives.
by Ali Hirsa
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Insurance Risk and Ruin,
Ideal for a first course in insurance risk theory. Includes numerous worked examples and exercises for which solutions are provided.
by David C. M. Dickson
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Handbook of Computational and Numerical Methods in Finance,
Numerical Methods in Finance have recently emerged as a new discipline at the intersection of probability theory, finance and numerical analysis. They bridge the gap between financial theory and computational practice and provide solutions to problems where analytical methods are often non-applicable. Numerical methods are more and more used in several topics of financial analy sis: computation of complex derivatives; market, credit and operational risk assess ment, asset liability management, optimal portfolio theory, financial econometrics and others. Although numerical methods in finance have been studied intensively in recent years, many theoretical and practical financial aspects have yet to be explored. This volume presents current research focusing on various numerical methods in finance. The contributions cover methodological issues. Genetic Algorithms, Neural Net works, Monte-Carlo methods, Finite Difference Methods, Stochastic Portfolio Opti mization as well as the application of other numerical methods in finance and risk management. As editor, I am grateful to the contributors for their fruitful collaboration. I would particularly like to thankStefan Trueck and Carlo Marinelli for the excellent editorial assistance received over the progress of this project. Thomas Plum did a splendid word-processingjob in preparing the manuscript. lowe much to George Anastassiou (ConsultantEditor, Birkhauser) and Ann Kostant Executive Editor, Mathematics and Physics, Birkhauser for their help and encouragement.
by Svetlozar T. Rachev
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Modelling Under Risk and Uncertainty,An Introduction to Statistical, Phenomenological and Computational Methods
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.
by Etienne de Rocquigny
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Random Evolutions and their Applications,New Trends
The book is devoted to the new trends in random evolutions and their various applications to stochastic evolutionary sytems (SES). Such new developments as the analogue of Dynkin's formulae, boundary value problems, stochastic stability and optimal control of random evolutions, stochastic evolutionary equations driven by martingale measures are considered. The book also contains such new trends in applied probability as stochastic models of financial and insurance mathematics in an incomplete market. In the famous classical financial mathematics Black-Scholes model of a (B,S) market for securities prices, which is used for the description of the evolution of bonds and stocks prices and also for their derivatives, such as options, futures, forward contracts, etc., it is supposed that the dynamic of bonds and stocks prices are set by a linear differential and linear stochastic differential equations, respectively, with interest rate, appreciation rate and volatility such that they are predictable processes. Also, in the Arrow-Debreu economy, the securities prices which support a Radner dynamic equilibrium are a combination of an Ito process and a random point process, with the all coefficients and jumps being predictable processes.
by Anatoly Swishchuk
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Handbook of Analytic Computational Methods in Applied Mathematics,
Working computationally in applied mathematics is the very essence of dealing with real-world problems in science and engineering. Approximation theory-on the borderline between pure and applied mathematics- has always supplied some of the most innovative ideas, computational methods, and original approaches to many types of problems. The first of its kind, the Handbook on Analytic-Computational Methods in Applied Mathematics comprises 22 self-contained chapters focused on various aspects of analytic computational methods in approximation theory and other related fields. The articles represent the leading research activities of contemporary, mainstream applied mathematics and address problems in a broad range of disciplines, from economics to statistics, dynamic programming, and engineering.
by George Anastassiou
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Computational Methods in Neural Modeling,7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003, Maó, Menorca, Spain, June 3-6. Proceedings
The two-volume set LNCS 2686 and LNCS 2687 constitute the refereed proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003, held in Mao, Menorca, Spain in June 2003. inclusion in the book and address the following topics: mathematical and computational methods in neural modelling, neurophysiological data analysis and modelling, structural and functional models of neurons, learning and other plasticity phenomena, complex systems dynamics, cognitive processes and artificial intelligence, methodologies for net design, bio-inspired systems and engineering, and applications in a broad variety of fields.
by José Mira
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Safety, Reliability and Risk Analysis,Theory, Methods and Applications (4 Volumes + CD-ROM)
Safety, Reliability and Risk Analysis. Theory, Methods and Applications contains the papers presented at the joint ESREL (European Safety and Reliability) and SRA-Europe (Society for Risk Analysis Europe) Conference (Valencia, Spain, 22-25 September 2008). The book covers a wide range of topics, including: Accident and Incident Investigation; Crisis and Emergency Management; Decision Support Systems and Software Tools for Safety and Reliability; Dynamic Reliability; Fault Identification and Diagnostics; Human Factors; Integrated Risk Management and Risk-Informed Decision-making; Legislative dimensions of risk management; Maintenance Modelling and Optimisation; Monte Carlo Methods in System Safety and Reliability; Occupational Safety; Organizational Learning; Reliability and Safety Data; Collection and Analysis; Risk and Evidence Based Policy Making; Risk and Hazard Analysis; Risk Control in Complex Environments; Risk Perception and Communication; Safety Culture; Safety Management Systems; Software Reliability; Stakeholder and public involvement in risk governance; Structural Reliability and Design Codes; System Reliability Analysis; Uncertainty and Sensitivity Analysis. Safety, Reliability and Risk Analysis. Theory, Methods and Applications will be of interest for academics and professionals working in a wide range of industrial and governmental sectors, including Aeronautics and Aerospace, Civil Engineering, Electrical and Electronic Engineering, Information Technology and Telecommunications, Insurance and Finance, Manufacturing, Mechanical Engineering, Nuclear Engineering, Policy Making and Public Planning.
by Sebastián Martorell
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Computational Methods in Band Theory,Proceedings of a Conference held at the IBM Thomas J. Watson Research Center, Yorktown Heights, New York, May 14–15, 1970, under the joint sponsorship of IBM and the American Physical Society
This volume contains the papers presented at the Conference on Computational Methods in Band Theory sponsored jointly by IBM and the American Physical Society and held at the IBM Thomas J. Watson Research Center, Yorktown Heights, New York, on May 14-15, 1970. The purpose of the conference was a sharing of information on the computational problems involved in relating models for the electron-electron and electron-ion interactions to experimentally measurable quantities. The papers comprising this volume therefore present up-to-date methodology for the calculation of single-particle energies and wave functions for periodic and near-periodic systems, the integration over these states required to describe experiment, and computationally practicable procedures for the introduction of exchange and correlation and the achievement of self-consistency. The proceedings is actually an expansion of the conference in that, unlike the oral presentations, the papers were not limited as to length. Furthermore, time was allowed after the conference to permit the papers to be written with the conference in retrospect, and five "prepared discussion" papers written by attendees of the conference but not on the original program are included. The latter are indicated in the table of contents by asterisks. The explicit emphasis of the conference on comparison of technique generated much lively argument, which is surely an indi cation of the current interest in the subject and the vigor of those working in it. It is our hope that the proceedings will make these comparisons available to the widest possible audience.
by Paul M. Marcus
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