Search results for "Downside risk"
showing 10 items of 13 documents
Comment on “A simple way to incorporate uncertainty and risk into forest harvest scheduling”
2017
In a recent research article, Robinson et al. (2016) described a method of estimating uncertainty of harvesting outcomes by analyzing the historical yield to the associated prediction for a large number of harvest operations. We agree with this analysis, and consider it a useful tool to integrate estimates of uncertainty into the optimization process. The authors attempt to manage the risk using two different methods, based on deterministic integer linear programming. The first method focused on maximizing the 10th quantile of the distribution of predicted volume subject to area constraint, while the second method focused on minimizing the variation of total quantity of volume harvested sub…
Fuzzy Portfolio Selection Models: A Numerical Study
2012
In this chapter we analyze the numerical performance of some possibilistic models for selecting portfolios in the framework of risk-return trade-off. Portfolio optimization deals with the problem of how to allocate wealth among several assets, taking into account the uncertainty involved in the behavior of the financial markets. Different approaches for quantifying the uncertainty of the future return on the investment are considered: either assuming that the return on every individual asset is modeled as a fuzzy number or directly measuring the uncertainty associated with the return on a given portfolio. Conflicting goals representing the uncertain return on and risk of a fuzzy portfolio a…
Exposure-Based Cash-Flow-At-Risk for Value-Creating Risk Management Under Macroeconomic Uncertainty
2010
A strategically minded CFO will realize that strategic corporate risk management is about finding the right balance between risk prevention and proactive value generation. Efficient risk and performance management requires adequate assessment of risk and risk exposures on the one hand and performance on the other. Properly designed, a risk measure should provide information on to what extend the firm's performance is at risk, what is causing that risk, the relative importance of non-value-adding and value-adding risk, and the possibilities to use risk management to reduce total risk. In this chapter, we present an approach – exposure-based cash-flow-at-risk – to calculating a firm's downsid…
The value of integrative risk management for insurance products with guarantees
2001
Insurance liabilities are converging with capital markets products (e.g. derivatives and securitizations), thereby increasing the demand for integrated asset and liability management strategies. This article compares the value-added by an integrative approach-based on scenario optimization modelling-relative to traditional risk management methods. The authors present some examples of products offered by the insurance industry in Italy, and apply the results of the analysis to the design of competitive insurance policies. © Emerald Backfiles 2007.
Scenario optimization asset and liability modelling for individual investors
2006
We develop a scenario optimization model for asset and liability management of individual investors. The individual has a given level of initial wealth and a target goal to be reached within some time horizon. The individual must determine an asset allocation strategy so that the portfolio growth rate will be sufficient to reach the target. A scenario optimization model is formulated which maximizes the upside potential of the portfolio, with limits on the downside risk. Both upside and downside are measured vis- `a-vis the goal. The stochastic behavior of asset returns is captured through bootstrap simulation, and the simulation is embedded in the model to determine the optimal portfolio. …
Uninformed Traders in European Stock Markets
2010
A fully informed agent bets with an uninformed over the capital gains of an asset. A divide-and-choose idea is adapted to induce both trade and revelation of information, but in equlibrium the uninformed buys high and sells low if he is downside risk averse. The result may be seen as an informed-price-maker counterpart of some findings of Glosten-Milgrom (1985) and Kyle (1985) on uninformed agents trading in financial markets.
A note on comparative downside risk aversion
2005
International audience; We provide comparative global conditions for downside risk aversion, which are similar to the ones studied by Ross for risk aversion. We define a coefficient of downside risk aversion, and study its local properties.
Fuzzy portfolio optimization under downside risk measures
2007
This paper presents two fuzzy portfolio selection models where the objective is to minimize the downside risk constrained by a given expected return. We assume that the rates of returns on securities are approximated as LR-fuzzy numbers of the same shape, and that the expected return and risk are evaluated by interval-valued means. We establish the relationship between those mean-interval definitions for a given fuzzy portfolio by using suitable ordering relations. Finally, we formulate the portfolio selection problem as a linear program when the returns on the assets are of trapezoidal form.
Portfolios with fuzzy returns: Selection strategies based on semi-infinite programming
2008
AbstractThis paper provides new models for portfolio selection in which the returns on securities are considered fuzzy numbers rather than random variables. The investor's problem is to find the portfolio that minimizes the risk of achieving a return that is not less than the return of a riskless asset. The corresponding optimal portfolio is derived using semi-infinite programming in a soft framework. The return on each asset and their membership functions are described using historical data. The investment risk is approximated by mean intervals which evaluate the downside risk for a given fuzzy portfolio. This approach is illustrated with a numerical example.
A multi-objective genetic algorithm for cardinality constrained fuzzy portfolio selection
2012
This paper presents a new procedure that extends genetic algorithms from their traditional domain of optimization to fuzzy ranking strategy for selecting efficient portfolios of restricted cardinality. The uncertainty of the returns on a given portfolio is modeled using fuzzy quantities and a downside risk function is used to describe the investor's aversion to risk. The fitness functions are based both on the value and the ambiguity of the trapezoidal fuzzy number which represents the uncertainty on the return. The soft-computing approach allows us to consider uncertainty and vagueness in databases and also to incorporate subjective characteristics into the portfolio selection problem. We …