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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.

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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.

Optimal Financial Decision Making Under Uncertainty | SpringerLink

We develop a stochastic programming framework for hedging currency and interest rate risk, with market traded currency forward contracts and interest rate swaps, in an environment with uncertain cash flows. The framework captures the skewness and kurtosis in exchange rates, transaction costs, the systematic risks in interest rates, and most importantly, the term premia which determine the expected cost of different hedging instruments. Given three commonly used objective functions: variance, expected shortfall, and mean log profits, we study properties of the optimal hedge.

We find that the choice of objective function can have a substantial effect on the resulting hedge in terms of the portfolio composition, the resulting risk and the hedging cost.

Decision Theory Under Uncertainty - Itzhak Gilboa

Further, we find that unless the objective is indifferent to hedging costs, term premia in the different markets, along with transaction costs, are fundamental determinants of the optimal hedge. Our results also show that to reduce risk properly and to keep hedging costs low, a rich-enough universe of hedging instruments is critical. Through out-of-sample testing we validate the findings of the in-sample analysis, and importantly, we show that the model is robust enough to be used on real market data.

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  • The proposed framework offers great flexibility regarding the distributional assumptions of the underlying risk factors and the types of hedging instruments which can be included in the optimization model. Please wait English Svenska Norsk.

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      Optimal Financial Decision Making under Uncertainty

      It requires the optimal control solution constructed using large number of Monte Carlo simulations of the main uncertainties e. Toggle navigation Show search form. Show search form. Dynamic Asset Allocation Strategy Conventional asset allocation strategies commonly used in the asset management industry are normally categorised as: 1.

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      An Analytic Platform for Decision-Making under Uncertainty To develop optimal decision strategies under uncertainty, to deliver application modules in the financial markets, and energy, mining, agriculture sectors. Optimal Decisions for Resource Extraction under Uncertainty CSIRO is developing methodologies and software for determining the switching boundaries to help mineral companies to make optimal sequential decisions under economic and geological uncertainties.