The First Collection That Covers This Field at the Dynamic Strategic and One-Period Tactical Levels Addressing the imbalance between research and practice, Quantitative Fund Management presents leading-edge theory and methods, along with their application in practical problems encountered in the fund management industry. A Current Snapshot of State-of-the-Art Applications of Dynamic Stochastic Optimization Techniques to Long-Term Financial Planning The first part of the book initially looks at how the quantitative techniques of the equity industry are shifting from basic Markowitz mean-variance portfolio optimization to risk management and trading applications. This section also explores novel aspects of lifetime individual consumption investment problems, fixed-mix portfolio rebalancing allocation strategies, debt management for funding mortgages and national debt, and guaranteed return fund construction. Up-to-Date Overview of Tactical Financial Planning and Risk Management The second section covers nontrivial computational approaches to tactical fund management. This part focuses on portfolio construction and risk management at the individual security or fund manager level over the period up to the next portfolio rebalance. It discusses non-Gaussian returns, new risk-return tradeoffs, and the robustness of benchmarks and portfolio decisions. The Future Use of Quantitative Techniques in Fund Management With contributions from well-known academics and practitioners, this volume will undoubtedly foster the recognition and wider acceptance of stochastic optimization techniques in financial practice.

This book gathers a selection of refereed papers presented at the "International Conference on Operations Research OR2015," which was held at the University of Vienna, Austria, September 1-4, 2015. Over 900 scientists and students from 50 countries attended this conference and presented more than 600 papers in parallel topic streams as well as special award sessions. Though the guiding theme of the conference was "Optimal Decision and Big Data," this volume also includes papers addressing practically all aspects of modern Operations Research.

The First Collection That Covers This Field at the Dynamic Strategic and One-Period Tactical Levels Addressing the imbalance between research and practice, Quantitative Fund Management presents leading-edge theory and methods, along with their application in practical problems encountered in the fund management industry. A Current Snapshot of State-of-the-Art Applications of Dynamic Stochastic Optimization Techniques to Long-Term Financial Planning The first part of the book initially looks at how the quantitative techniques of the equity industry are shifting from basic Markowitz mean-variance portfolio optimization to risk management and trading applications. This section also explores novel aspects of lifetime individual consumption investment problems, fixed-mix portfolio rebalancing allocation strategies, debt management for funding mortgages and national debt, and guaranteed return fund construction. Up-to-Date Overview of Tactical Financial Planning and Risk Management The second section covers nontrivial computational approaches to tactical fund management. This part focuses on portfolio construction and risk management at the individual security or fund manager level over the period up to the next portfolio rebalance. It discusses non-Gaussian returns, new risk-return tradeoffs, and the robustness of benchmarks and portfolio decisions. The Future Use of Quantitative Techniques in Fund Management With contributions from well-known academics and practitioners, this volume will undoubtedly foster the recognition and wider acceptance of stochastic optimization techniques in financial practice.

Stefan Hochrainer develops a catastrophe risk management model. It illustrates which trade-offs and choices a country must make in managing economic risks due to natural disasters. Budgetary resources are allocated to pre-disaster risk management strategies to reduce the probability of financing gaps. The framework and model approach allows cross country comparisons as well as the assessment of financial vulnerability, macroeconomic risk, and risk management strategies. Three case studies demonstrate its flexibility and coherent approach.

Ongoing global changes bring fundamentally new scientific problems requiring
new concepts and tools. A key issue concerns a vast variety of practically
irreducible uncertainties, which challenge our traditional models and require
new concepts and analytical tools. The uncertainty critically dominates, e.g.,
the climate change debates. In short, the dilemma is concerned with enormous
costs vs. massive uncertainties of potential extreme impacts.
Traditional scientific approaches usually rely on real observations and
experiments. Yet no sufficient observations exist for new problems, and "pure"
experiments and learning by doing may be very expensive, dangerous, or
simply impossible. In addition, available historical observations are contaminated
by actions, policies. The complexity of new problems does not allow to achieve
enough certainty by increasing the resolution of models or by bringing in more links.
Hence, new tools for modeling and management of uncertainty are needed, as given
in this book which was prepared for an interdisciplinary audience, and addresses open
problems, limitations of known approaches, novel methods and techniques, or lessons
from the applications of various approaches. Thus, the book contributes to a better
understanding between practitioners dealing with the management of uncertainty, and
scientists working on either corresponding modeling approaches that can be applied for
improving understanding or management of uncertainty.

This volume contains selected papers presented at the "International Workshop on Computationally Intensive Methods in Simulation and Op th th timization" held from 23 to 25 August 1990 at the International Institute for Applied Systems Analysis (nASA) in La~enburg, Austria. The purpose of this workshop was to evaluate and to compare recently developed methods dealing with optimization in uncertain environments. It is one of the nASA's activities to study optimal decisions for uncertain systems and to make the result usable in economic, financial, ecological and resource planning. Over 40 participants from 12 different countries contributed to the success of the workshop, 12 papers were selected for this volume. Prof. A. Kurzhanskii Chairman of the Systems and Decision Sciences Program nASA Preface Optimization in an random environment has become an important branch of Applied Mathematics and Operations Research. It deals with optimal de cisions when only incomplete information of t.he future is available. Consider the following example: you have to make the decision about the amount of production although the future demand is unknown. If the size of the de mand can be described by a probability distribution, the problem is called a stochastic optimization problem.