Search results for " Uncertainty"
showing 10 items of 777 documents
Simulating term structure of interest rates with arbitrary marginals
2011
Decision models under uncertainty rely their analysis on scenarios of the economic factors. A key economic factor is the term structure of interest rates (yields). Simulation models of the yield curve usually assume that the conjugate distribution of the interest rates is lognormal. Dynamic models, like vector auto-regression, implicitly postulate that the logarithm of the interest rates is normally distributed. Statistical analyses have, however, shown that stationary transformations (yield changes) of the interest rates are substantially leptokurtic, thus posing serious doubts on the reliability of the available models. We propose in this paper a VARTA model (Biller and Nelson, 2003) to s…
Prevalence of unwillingness and uncertainty to vaccinate against COVID-19 in older people: A systematic review and meta-analysis.
2021
The coronavirus disease 2019 (COVID-19) has been shown to have more severe health outcomes in older people specifically in relation to mortality and disability. Vaccination seems to be efficacious and safe for preventing the negative consequences of COVID-19, but vaccine hesitancy seems to be high in older adults. We therefore aimed to investigate the prevalence of unwillingness and the uncertainty to vaccinate against COVID-19 in older people and the factors that can be associated with the unwillingness to vaccinate. For this work, we searched several databases until 18th June 2021 for studies reporting the prevalence of unwillingness and the uncertainty to vaccinate against COVID-19 in pe…
Les followers ont-ils vraiment de l'importance dans le modèle de Stackelberg?
2011
In this paper, we consider a T-stage linear model of Stackelberg oligopoly. First, we show geometrically and analytically that under the two conditions of linear market demand and identical constant marginal costs, the T-stage Stackelberg model reduces to a model where T oligopolies exploit residual demand sequentially. At any stage, leaders behave as if followers did not matter. Second, we study social welfare and convergence toward competitive equilibrium. Especially, we consider the velocity of convergence as the number of firms increases. The convergence is faster when reallocating firms from the most to the less populated cohort until equalizing the size of all cohorts.
Minimising value-at-risk in a portfolio optimisation problem using a multi-objective genetic algorithm
2011
[EN] In this paper, we develop a general framework for market risk optimisation that focuses on VaR. The reason for this choice is the complexity and problems associated with risk return optimisation (non-convex and non-differential objective function). Our purpose is to obtain VaR efficient frontiers using a multi-objective genetic algorithm (GA) and to show the potential utility of the algorithm to obtain efficient portfolios when the risk measure does not allow calculating an optimal solution. Furthermore, we measure differences between VaR efficient frontiers and variance efficient frontiers in VaR-return space and we evaluate out-sample capacity of portfolios on both bullish and bearis…
Statistical formats to optimize evidence-based decision making: A behavioral approach
2013
Abstract Statistical information is crucial for managerial decision making. The decision-making literature in psychology and mathematical cognition documents how different statistical formats can facilitate certain types of decisions. The present analysis is the first of its kind to assess the impact of statistical formats in the presentation of data from market research on both the optimality of market decisions and the time required to perform the decision-making process. An economic experiment provides the data for this study. The experiment presents statistical information in simple frequencies and relative frequencies using numerical and pictorial representations in the context of diff…
A Simple Indicator Based Evolutionary Algorithm for Set-Based Minmax Robustness
2018
For multiobjective optimization problems with uncertain parameters in the objective functions, different variants of minmax robustness concepts have been defined in the literature. The idea of minmax robustness is to optimize in the worst case such that the solutions have the best objective function values even when the worst case happens. However, the computation of the minmax robust Pareto optimal solutions remains challenging. This paper proposes a simple indicator based evolutionary algorithm for robustness (SIBEA-R) to address this challenge by computing a set of non-dominated set-based minmax robust solutions. In SIBEA-R, we consider the set of objective function values in the worst c…
Modelling agricultural risk in a large scale positive mathematical programming model
2020
International audience; Mathematical programming has been extensively used to account for risk in farmers' decision making. The recent development of the positive mathematical programming (PMP) has renewed the need to incorporate risk in a more robust and flexible way. Most of the existing PMP-risk models have been tested at farm-type level and for a very limited sample of farms. This paper presents and tests a novel methodology for modelling risk at individual farm level in a large scale model, called individual farm model for common agricultural policy analysis (IFM-CAP). Results show a clear trade-off between including and excluding the risk specification. Albeit both alternatives provid…
Multi-scenario multi-objective robust optimization under deep uncertainty: A posteriori approach
2021
This paper proposes a novel optimization approach for multi-scenario multi-objective robust decision making, as well as an alternative way for scenario discovery and identifying vulnerable scenarios even before any solution generation. To demonstrate and test the novel approach, we use the classic shallow lake problem. We compare the results obtained with the novel approach to those obtained with previously used approaches. We show that the novel approach guarantees the feasibility and robust efficiency of the produced solutions under all selected scenarios, while decreasing computation cost, addresses the scenario-dependency issues, and enables the decision-makers to explore the trade-off …
Global sensitivity analysis for urban water quality modelling: Terminology, convergence and comparison of different methods
2015
Abstract Sensitivity analysis represents an important step in improving the understanding and use of environmental models. Indeed, by means of global sensitivity analysis (GSA), modellers may identify both important ( factor prioritisation ) and non-influential ( factor fixing ) model factors. No general rule has yet been defined for verifying the convergence of the GSA methods. In order to fill this gap this paper presents a convergence analysis of three widely used GSA methods (SRC, Extended FAST and Morris screening) for an urban drainage stormwater quality–quantity model. After the convergence was achieved the results of each method were compared. In particular, a discussion on peculiar…
A Conditional Value–at–Risk Model for Insurance Products with Guarantee
2009
We propose a model to select the optimal portfolio which underlies insurance policies with a guarantee. The objective function is defined in order to minimise the conditional value at-risk (CVaR) of the distribution of the losses with respect to a target return. We add operational and regulatory constraints to make the model as flexible as possible when used for real applications. We show that the integration of the asset and liability side yields superior performances with respect to naive fixed-mix portfolios and asset based strategies. We validate the model on out-of-sample scenarios and provide insights on policy design.