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Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk
The value-at-risk (VaR) and the conditional value-at-risk (CVaR) are two commonly used risk measures. We state some of their properties and make a comparison. Moreover, the structure of the portfolioExpand
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Increasing stress on disaster-risk finance due to large floods
An assessment of economic flood risk trends across Europe reveals high current and future stress on risk financing schemes. The magnitude and distribution of losses can be contained by investing inExpand
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Scenario tree generation for multiperiod financial optimization by optimal discretization
  • G. Pflug
  • Mathematics, Computer Science
  • Math. Program.
  • 2001
We show how a scenario tree may be constructed in an optimal manner on the basis of a simulation model of the underlying financial process by using a stochastic approximation technique. Expand
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Modeling, Measuring and Managing Risk
This book is the first in the market to treat single- and multi-period risk measures (risk functionals) in a thorough, comprehensive manner. It combines the treatment of properties of the riskExpand
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Value-at-Risk in Portfolio Optimization: Properties and Computational Approach ⁄
Value-at-Risk (VAR) is an important and widely used measure of the extent to which a given portfolio is subject to risk present in financial markets. In this paper, we present a method of calculatingExpand
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Optimization of Stochastic Models
Stochastic models are everywhere. In manufacturing, queuing models are used for modeling production processes, realistic inventory models are stochastic in nature. Stochastic models are considered inExpand
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Multistage Stochastic Optimization
Introduction.- The Nested Distance.- Risk and Utility Functionals.- From Data to Models.- Time Consistency.- Approximations and Bounds.- The Problem of Ambiguity in Stochastic Optimization.- Examples.
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A branch and bound method for stochastic global optimization
A stochastic branch and bound method for solving global optimization problems is proposed for partitioning the feasible set into compact subsets. Expand
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