We use a fairly general framework to analyze a rich variety of financial optimization models presented in the literature, with emphasis on contributions included in this volume and a related special issue of OR Spectrum. We do not aim at providing readers with an exhaustive survey, rather we focus on a limited but significant set of modeling and methodological issues. The framework is based on a benchmark discrete-time stochastic control optimization framework, and a benchmark financial problem, asset--liability management, whose generality is considered in this chapter. A wide set of financial problems, ranging from asset allocation to financial engineering problems, is outlined, in terms of objectives, risk models, solution methods, and m...
In this diploma paper we discuss selected optimization methods and mathematical programming models. ...
It is unrealistic to formulate the problems arising under uncertain environments as deterministic op...
A review of some of the most important existing parallel solution algorithms for stochastic dynamic ...
We use a fairly general framework to analyze a rich variety of financial optimization models presen...
We use a fairly general framework to analyze a rich variety of financial optimization models presen...
This thesis addresses the topic of decision making under uncertainty, with particular focus on finan...
Stochastic optimization is an effective tool for analyzing decision problems under uncertainty. In s...
This work is focused on models of optimal asset and liability management. The practical section illu...
Practical portfolio investment problems under uncertainty can be modeled well as multiperiod stochas...
This project covers the basics of Financial Portfolio Management theory through different stochastic...
Research conducted in mathematical finance focuses on the quantitative modeling of financial markets...
Many financial optimization problems involve future values of security prices, interest rates and ex...
In this chapter, we are concerned with decision making methods for dynamic systems under uncertainty...
Two different stochastic decision models are developed for incorporating uncertainty and risk aversi...
This entry considers the problem of a typical pension fund that collects premiums from sponsors or e...
In this diploma paper we discuss selected optimization methods and mathematical programming models. ...
It is unrealistic to formulate the problems arising under uncertain environments as deterministic op...
A review of some of the most important existing parallel solution algorithms for stochastic dynamic ...
We use a fairly general framework to analyze a rich variety of financial optimization models presen...
We use a fairly general framework to analyze a rich variety of financial optimization models presen...
This thesis addresses the topic of decision making under uncertainty, with particular focus on finan...
Stochastic optimization is an effective tool for analyzing decision problems under uncertainty. In s...
This work is focused on models of optimal asset and liability management. The practical section illu...
Practical portfolio investment problems under uncertainty can be modeled well as multiperiod stochas...
This project covers the basics of Financial Portfolio Management theory through different stochastic...
Research conducted in mathematical finance focuses on the quantitative modeling of financial markets...
Many financial optimization problems involve future values of security prices, interest rates and ex...
In this chapter, we are concerned with decision making methods for dynamic systems under uncertainty...
Two different stochastic decision models are developed for incorporating uncertainty and risk aversi...
This entry considers the problem of a typical pension fund that collects premiums from sponsors or e...
In this diploma paper we discuss selected optimization methods and mathematical programming models. ...
It is unrealistic to formulate the problems arising under uncertain environments as deterministic op...
A review of some of the most important existing parallel solution algorithms for stochastic dynamic ...