This first volume of the Handbook of Asset and Liability Management presents the theories and methods supporting models that align a firm's operations and tactics with its uncertain environment. Detailing the symbiosis between optimization tools and financial decision-making, its original articles cover term and volatility structures, interest rates, risk-return analysis, dynamic asset allocation strategies in discrete and continuous time, the use of stochastic programming models, bond portfolio management, and the Kelly capital growth theory and practice. They effectively set the scene for Volume Two by showing how the management of risky assets and uncertain liabilities within an integrated, coherent framework remains the core problem for both financial institutions and other business enterprises as well.
*Each volume presents an accurate survey of a sub-field of finance
*Fills a substantial gap in this field
*Broad in scope

The Handbooks in Finance are intended to be a definitive source for comprehensive and accessible information in the field of finance. Each individual volume in the series presents an accurate self-contained survey of a sub-field of finance, suitable for use by finance and economics professors and lecturers, professional researchers, graduate students and as a teaching supplement.
It is fitting that the series Handbooks in Finance devotes a handbook to Asset and Liability Management. Volume 2 focuses on applications and case studies in asset and liability management.
The growth in knowledge about practical asset and liability modeling has followed the popularity of these models in diverse business settings. This volume portrays ALM in practice, in contrast to Volume 1, which addresses the theories and methodologies behind these models. In original articles practitioners and scholars describe and analyze models used in banking, insurance, money management, individual investor financial planning, pension funds, and social security. They put the traditional purpose of ALM, to control interest rate and liquidity risks, into rich and broad-minded frameworks. Readers interested in other business settings will find their discussions of financial institutions both instructive and revealing.
* Focuses on pragmatic applications
* Relevant to a variety of risk-management industries
* Analyzes models used in most financial sectors

This book offers a unique pathway to methods of parallel optimization by introducing parallel computing ideas and techniques into both optimization theory, and into some numerical algorithms for large-scale optimization problems. The presentation is based on the recent understanding that rigorous mathematical analysis of algorithms, parallel computing techniques, and "hands-on" experimental work on real-world problems must go hand in hand in order to achieve the greatest advantage from novel parallel computing architectures. The three parts of the book thus bring together relevant theory, careful study of algorithms, and modelling of significant real world problems. The problem domains include: image reconstruction, radiation therapy treatment planning, transportation problems, portfolilo management, and matrix estimation. This text can be used both as a reference for researchers and as a text for advanced graduate courses.

The articles included in the volume cover a range of diverse topics linked by a common theme: the use of formal modelling techniques to promote better understanding of financial markets and improve management of financial operations.
Apart from a theoretical discussion, most of the papers model validation or verification using market data. This collection of articles sets the framework for other studies that could link theory and practice.

The use of mathematical models in financial management is today common business practice. The state of the art is constantly being advanced by academia and refined by industry. This book achieves two objectives. First, it brings together the (apparently) diverse fields of finance and management science/operations research. It presents a variety of techniques used in complex problems for financial management: optimization, simulation, stochastic programming and supercomputing. Second, it links current industrial practices with academic research to a degree unparalleled by any previous publication in the field.