WorldCat Identities

Kaut, Michal

Overview
Works: 7 works in 9 publications in 1 language and 14 library holdings
Roles: Author, Other, opp
Publication Timeline
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Most widely held works by Michal Kaut
The impact of uncertainty on the European energy market a scenario aggregation approach by Kjell Arne Brekke( Book )

3 editions published in 2013 in English and held by 3 WorldCat member libraries worldwide

We present a simple approach to transform a deterministic numerical model, where several agents simultaneously make decisions, into a stochastic model. This approach, which builds on scenario aggregation, a numerical method developed to solve decision problems under uncertainty, is used to build a large stochastic numerical equilibrium model of the Western European energy markets. We use the stochastic model to analyze the impact of economic and political uncertainty on the Western European energy markets. We demonstrate that the equilibria under uncertainty differ significantly from the deterministic outcomes
Shape-based Scenario Generation using Copulas by Michal Kaut( )

1 edition published in 2006 in English and held by 2 WorldCat member libraries worldwide

Hydropower bidding in a multi-market setting by Ellen Krohn Aasgård( )

1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide

Stochastic optimization models for a single-sink transportation problem by Francesca Maggioni( )

1 edition published in 2008 in English and held by 2 WorldCat member libraries worldwide

A heuristic for generating scenario trees for multistage decision problems by Kjetil Høyland( )

1 edition published in 2001 in English and held by 2 WorldCat member libraries worldwide

Evaluation of scenario-generation methods for stochastic programming by Michal Kaut( )

1 edition published in 2003 in English and held by 2 WorldCat member libraries worldwide

Decision Making under Uncertainty in Financial Markets Improving Decisions with Stochastic Optimization by Jonas Ekblom( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

This thesis addresses the topic of decision making under uncertainty, with particular focus on financial markets. The aim of this research is to support improved decisions in practice, and related to this, to advance our understanding of financial markets. Stochastic optimization provides the tools to determine optimal decisions in uncertain environments, and the optimality conditions of these models produce insights into how financial markets work. To be more concrete, a great deal of financial theory is based on optimality conditions derived from stochastic optimization models. Therefore, an important part of the development of financial theory is to study stochastic optimization models that step-by-step better capture the essence of reality. This is the motivation behind the focus of this thesis, which is to study methods that in relation to prevailing models that underlie financial theory allow additional real-world complexities to be properly modeled. The overall purpose of this thesis is to develop and evaluate stochastic optimization models that support improved decisions under uncertainty on financial markets. The research into stochastic optimization in financial literature has traditionally focused on problem formulations that allow closed-form or `exact' numerical solutions; typically through the application of dynamic programming or optimal control. The focus in this thesis is on two other optimization methods, namely stochastic programming and approximate dynamic programming, which open up opportunities to study new classes of financial problems. More specifically, these optimization methods allow additional and important aspects of many real-world problems to be captured. This thesis contributes with several insights that are relevant for both financial and stochastic optimization literature. First, we show that the modeling of several real-world aspects traditionally not considered in the literature are important components in a model which supports corporate hedging decisions. Specifically, we document the importance of modeling term premia, a rich asset universe and transaction costs. Secondly, we provide two methodological contributions to the stochastic programming literature by: (i) highlighting the challenges of realizing improved decisions through more stages in stochastic programming models; and (ii) developing an importance sampling method that can be used to produce high solution quality with few scenarios. Finally, we design an approximate dynamic programming model that gives close to optimal solutions to the classic, and thus far unsolved, portfolio choice problem with constant relative risk aversion preferences and transaction costs, given many risky assets and a large number of time periods
 
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