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Liquidity Risk Measurement and Management
Major events such as the Asian crisis in 1997, the Russian default on short-term debt in 1998, the downfall of the hedge fund long-term capital management in 1998 and the disruption in payment systems following the World Trade Center attack in 2001, all resulted in increased management’s attention to liquidity risk. Banks have realized that adequate systems and processes for identifying, measuring, monitoring and controlling liquidity risks help them to maintain a strong liquidity position, which in turn will increase the confidence of investors and rating agencies as well as improve funding costs and availability. Liquidity Risk Measurement and Management: A Practitioner’s Guide to Global Best Practices provides the best practices in tools and techniques for bank liquidity risk measurement and management. Experienced bankers and highly regarded liquidity risk experts share their insights and practical experiences in this book.
Stochastic Programming
From the Preface... The preparation of this book started in 2004, when George B. Dantzig and I, following a long-standing invitation by Fred Hillier to contribute a volume to his International Series in Operations Research and Management Science, decided finally to go ahead with editing a volume on stochastic programming. The field of stochastic programming (also referred to as optimization under uncertainty or planning under uncertainty) had advanced significantly in the last two decades, both theoretically and in practice. George Dantzig and I felt that it would be valuable to showcase some of these advances and to present what one might call the state-of- the-art of the field to a broader...
Applications of Stochastic Programming
Consisting of two parts, this book presents papers describing publicly available stochastic programming systems that are operational. It presents a diverse collection of application papers in areas such as production, supply chain and scheduling, gaming, environmental and pollution control, financial modeling, telecommunications, and electricity.
Optimization in the Energy Industry
This book offers a broad, in-depth overview that reflects the requirements, possibilities and limits of mathematical optimization and, especially, stochastic optimization in the energy industry.
System Modelling and Optimization
System Modelling and Optimization covers research issues within systems theory, optimization, modelling, and computing. It includes contributions to structural mechanics, integer programming, nonlinear programming, interior point methods, dynamical systems, stability analysis, stochastic optimization, bilevel optimization, and semidefinite programming. Several survey papers written by leading experts in their fields complement new developments in theory and applications. This book contains most of the invited papers and a few carefully selected submitted papers that were presented at the 19th IFIP TC7 Conference on System Modelling and Optimization, which was held in Cambridge, England, from July 12 to 16, 1999, and sponsored by the International Federation for Information Processing (IFIP).
Worldwide Asset and Liability Modeling
The underlying theme of this volume is how to invest assets over time to achieve satisfactory returns subject to uncertainties, various constraints and liability commitments. Most investors, be they individuals or institutions, do not diversify properly across markets nor across time. The papers utilize several approaches and integrate a number of techniques as well as discussing a variety of models that have either been implemented, are close to being implemented, or represent new innovative approaches that may lead to future novel applications. Other issues address the future of asset-liability management modeling. This includes models for individuals, and various financial institutions such as banks and insurance companies. This will lead to custom products, that is, financial engineering. All in all, this will be essential reading for all involved in analysing the financial markets.
Probabilistic Constrained Optimization
Probabilistic and percentile/quantile functions play an important role in several applications, such as finance (Value-at-Risk), nuclear safety, and the environment. Recently, significant advances have been made in sensitivity analysis and optimization of probabilistic functions, which is the basis for construction of new efficient approaches. This book presents the state of the art in the theory of optimization of probabilistic functions and several engineering and finance applications, including material flow systems, production planning, Value-at-Risk, asset and liability management, and optimal trading strategies for financial derivatives (options). Audience: The book is a valuable source of information for faculty, students, researchers, and practitioners in financial engineering, operation research, optimization, computer science, and related areas.
The Measurement of Market Risk
This book is a revised version of my doctoral dissertation submitted to the University of St. Gallen in October 1999. I would like to thank Dr. oec. Marc Wildi whose careful reading of much of the text led to many improvements. All errors remain mine. Pfiiffikon SZ, Switzerland, March 2001 Pierre-Yves Moix Preface to the dissertation "Education is man's going forward from cocksure ignorance to thoughtful uncertainty" Don Clark's Scrapbook quoted in Wonnacott and Wonnacott (1990). After several years of banking practice, I decided to give up some of my certitudes and considered this thesis project a good opportunity to study some of the quantitative tools necessary for the modelling of uncert...
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Stochastic Optimization
Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering.
