97 research outputs found

    Uncertainty Aversion and Robust Portfolio Choices

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    Optimal portfolio rules are derived under uncertainty aversion by formulating the portfolio choice problem as a robust control problem. Using a power utility function of the form C with 0Uncertainty Aversion, Model Misspeci…cation, Robust Control, Portfolio Choice Models

    Uncertainty Aversion, Robust Control and Asset Holdings

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    Optimal portfolio rules are derived under uncertainty aversion by formulating the portfolio choice problem as a robust control problem. The robust portfolio rule indicates that the total holdings of risky assets as a proportion of the investor’s wealth could increase as compared to the holdings under the Merton rule, which is the standard risk aversion case. With two risky assets an increase in the holdings of the one risky asset is accompanied by a reduction in the holdings of the other asset. Furthermore, in the optimal robust portfolio the investor may increase the holdings of the asset for which there is or less ambiguity, and reduce the holding of the asset for which there is more ambiguity, a result that might provide an explanation of the home bias puzzle.Uncertainty aversion, Model misspecification, Robust control, Portfolio choice models

    Model Uncertainty, Ambiguity and the Precautionary Principle: Implications for Biodiversity Management

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    We analyze ecosystem management under `unmeasurable' Knightian uncertainty or ambiguity which, given the uncertainties characterizing ecosystems, might be a more appropriate framework relative to the classic risk case (measurable uncertainty). This approach is used as a formal way of modelling the precautionary principle in the context of least favorable priors and maxmin criteria. We provide biodiversity management rules which incorporate the precautionary principle. These rules take the form of either safety margins and minimum safety standards or optimal harvesting under precautionary approaches.Knightian uncertainty, ambiguity, risk, precautionary principle, biodiversity management, optimal harvesting, robust control

    Uncertainty Aversion, Robust Control and Asset Holdings

    Get PDF
    Optimal portfolio rules are derived under uncertainty aversion by formulating the portfolio choice problem as a robust control problem. The robust portfolio rule indicates that the total holdings of risky assets as a proportion of the investor’s wealth could increase as compared to the holdings under the Merton rule, which is the standard risk aversion case. In particular, with two risky assets and one risk-free asset, we show that uncertainty aversion could lead to an increase in the holdings of the one risky asset, accompanied by a reduction in the holdings of the other risky asset. Furthermore, in the optimal robust portfolio the investor may increase the holdings of the asset for which there is or less ambiguity, and reduce the holdings of the asset for which there is more ambiguity, a result that might provide an explanation of the home bias puzzle.Uncertainty Aversion, Model Misspecification, Robust Control, Portfolio Choice Models

    Model Uncertainty, Ambiguity and the Precautionary Principle: Implications for Biodiversity Management

    Get PDF
    We analyze ecosystem management under `unmeasurable' Knightian uncertainty or ambiguity which, given the uncertainties characterizing ecosystems, might be a more appropriate framework relative to the classic risk case (measurable uncertainty). This approach is used as a formal way of modelling the precautionary principle in the context of least favorable priors and maxmin criteria. We provide biodiversity management rules which incorporate the precautionary principle. These rules take the form of either safety margins and minimum safety standards or optimal harvesting under precautionary approaches

    Investments in Gas Pipelines and Liquefied Natural Gas Infrastructure. What is the Impact on the Security of Supply?

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    Natural Resources, Investment and Long-Term Income

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    On Coalition Formation with Heterogeneous Agents

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