Ebook: Probabilistic Constrained Optimization: Methodology and Applications

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.

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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.

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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.
Content:
Front Matter....Pages i-xii
Introduction to the Theory of Probabilistic Functions and Percentiles (Value-at-Risk)....Pages 1-25
Pricing American Options by Simulation Using a Stochastic Mesh with Optimized Weights....Pages 26-44
On Optimization of Unreliable Material Flow Systems....Pages 45-66
Stochastic Optimization in Asset & Liability Management: A Model for Non-Maturing Accounts....Pages 67-101
Optimization in the Space of Distribution Functions and Applications in the Bayes Analysis....Pages 102-131
Sensitivity Analysis of Worst-Case Distribution for Probability Optimization Problems....Pages 132-147
On Maximum Reliability Problem in Parallel-Series Systems with Two Failure Modes....Pages 148-159
Robust Monte Carlo Simulation for Approximate Covariance Matrices and VaR Analyses....Pages 160-172
Structure of Optimal Stopping Strategies for American Type Options....Pages 173-185
Approximation of Value-at-Risk Problems with Decision Rules....Pages 186-197
Managing Risk with Expected Shortfall....Pages 198-219
On the Numerical Solution of Jointly Chance Constrained Problems....Pages 220-235
Management of Quality of Service through Chance-constraints in Multimedia Networks....Pages 236-251
Solution of a Product Substitution Problem Using Stochastic Programming....Pages 252-271
Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk....Pages 272-281
Statistical Inference of Stochastic Optimization Problems....Pages 282-307

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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.
Content:
Front Matter....Pages i-xii
Introduction to the Theory of Probabilistic Functions and Percentiles (Value-at-Risk)....Pages 1-25
Pricing American Options by Simulation Using a Stochastic Mesh with Optimized Weights....Pages 26-44
On Optimization of Unreliable Material Flow Systems....Pages 45-66
Stochastic Optimization in Asset & Liability Management: A Model for Non-Maturing Accounts....Pages 67-101
Optimization in the Space of Distribution Functions and Applications in the Bayes Analysis....Pages 102-131
Sensitivity Analysis of Worst-Case Distribution for Probability Optimization Problems....Pages 132-147
On Maximum Reliability Problem in Parallel-Series Systems with Two Failure Modes....Pages 148-159
Robust Monte Carlo Simulation for Approximate Covariance Matrices and VaR Analyses....Pages 160-172
Structure of Optimal Stopping Strategies for American Type Options....Pages 173-185
Approximation of Value-at-Risk Problems with Decision Rules....Pages 186-197
Managing Risk with Expected Shortfall....Pages 198-219
On the Numerical Solution of Jointly Chance Constrained Problems....Pages 220-235
Management of Quality of Service through Chance-constraints in Multimedia Networks....Pages 236-251
Solution of a Product Substitution Problem Using Stochastic Programming....Pages 252-271
Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk....Pages 272-281
Statistical Inference of Stochastic Optimization Problems....Pages 282-307
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