We discuss a natural way to generate the μ-term in supergravity scenario. Once the supergravity effects are taken into account, the vacuum expectation values (VEVs) of the heavy fields are in general shifted from the values in the supersymmetric limit. We note that this fact is independent of any Kahler ansatz and the values of the VEV shifts are of the order of the gravitino mass. As an exampl...

A statistical model is developed to generate probability based scenarios for forecasting and risk management of long term risks. The model forecasts transformed daily forward interest rates over a 10 year horizon. The model is a reduced rank vector autoregression with time varying volatilities and correlations. A quasi-differencing version reduces the impact of autocorrelated measurement errors...

ions and notations for the domain at hand. They do not support specific DL patterns, models, and methodologies. There are few software toolkits available to build DLs. As a result, most DLs are custom-built using homegrown architectures without making use of conceptual modeling, requirements analysis, and methodological approaches. The general trend has been to develop tools to solve small part...

In most practical applications of stochastic programming, we have to approximate the probability distribution of the stochastic parameters by a discrete distribution, i.e. a list of realizations (scenarios) and their probabilities. The process of generating this discrete distribution is usually referred to as ‘scenario generation’. As with any other approximation, the quality of the scenarios i...

Multi-stage financial decision optimization under uncertainty depends on a careful numerical approximation of the underlying stochastic process, which describes the future returns of the selected assets or asset categories. Various approaches towards an optimal generation of discrete-time, discrete-state approximations (represented as scenario trees) have been suggested in the literature. In th...

In single-period portfolio optimization several facets of the problem may influence the goodness of the portfolios selected. Despite that, some of these facets are frequently ignored when the optimization problem is solved. In this thesis, we aim at investigating the impact of these facets on the optimization problem and on the performances of the portfolios selected. Firstly, we consider the p...

In this paper, we present a method for generating scenarios for two-stage stochastic programs, using multivariate distributions specified by their marginal distributions and the correlation matrix. The margins are described by their cumulative distribution functions and we allow each margin to be of different type. We demonstrate the method on a model from stochastic service network design and ...

Sometimes, the best available information about an uncertain future is a single forecast. On the other hand, stochastic-programming models need future data in the form of scenario trees. While a single forecast does not provide enough information to construct a scenario tree, a forecast combined with historical data does—but none of the standard scenario-generation methods is suited to handle t...