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 ...
In 2002, Korn and Wilmott introduced the worst-case scenario optimal portfolio approach. They exten...
Many financial optimization problems involve future values of security prices, interest rates and ex...
Two different stochastic decision models are developed for incorporating uncertainty and risk aversi...
We use a fairly general framework to analyze a rich variety of financial optimization models present...
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...
Research conducted in mathematical finance focuses on the quantitative modeling of financial markets...
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...
In this chapter, we are concerned with decision making methods for dynamic systems under uncertainty...
The problem of investing money is common to citizens, families and companies. In this chapter, we in...
The topic of this thesis is portfolio optimization under model ambiguity, i.e. a situation when the ...
This entry considers the problem of a typical pension fund that collects premiums from sponsors or e...
In 2002, Korn and Wilmott introduced the worst-case scenario optimal portfolio approach. They exten...
Many financial optimization problems involve future values of security prices, interest rates and ex...
Two different stochastic decision models are developed for incorporating uncertainty and risk aversi...
We use a fairly general framework to analyze a rich variety of financial optimization models present...
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...
Research conducted in mathematical finance focuses on the quantitative modeling of financial markets...
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...
In this chapter, we are concerned with decision making methods for dynamic systems under uncertainty...
The problem of investing money is common to citizens, families and companies. In this chapter, we in...
The topic of this thesis is portfolio optimization under model ambiguity, i.e. a situation when the ...
This entry considers the problem of a typical pension fund that collects premiums from sponsors or e...
In 2002, Korn and Wilmott introduced the worst-case scenario optimal portfolio approach. They exten...
Many financial optimization problems involve future values of security prices, interest rates and ex...
Two different stochastic decision models are developed for incorporating uncertainty and risk aversi...