Index tracking (IT) is an investment strategy aimed at replicating the performance of a given financial index, taken as benchmark, over a given time horizon. This paper deals with the IT problem by proposing a stochas...
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ISBN:
(纸本)9789897583520
Index tracking (IT) is an investment strategy aimed at replicating the performance of a given financial index, taken as benchmark, over a given time horizon. This paper deals with the IT problem by proposing a stochastic programming model where the tracking error is measured by the Conditional Value at Risk (CVaR) measure. The multistage formulation overcomes the myopic view of the static models considering a longer time horizon and provides a more flexible paradigm where the initial strategy can be revised to account for changed market conditions. The proposed formulation presents a bi-objective function, where the two conflicting criteria wealth maximization and risk minimization. are jointly accounted for by properly choosing the weight to attribute to the two terms. The model is encapsulated within a rolling horizon scheme and solved iteratively exploiting each time the more update information in the generation of the scenario tree. The preliminary computational experiments carried out by considering as benchmark the Italian index FSTE-MIB seem to be promising and show that, on an out-of-sample analysis. the tracking portfolios follow the benchmark very closely, overcoming it on the long run.
The drone swarm that performs missions in an autonomous and intelligent overall collaborative manner is an important operational factor in the complex flight space. However, the complex and changing battlefield enviro...
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ISBN:
(纸本)9781728129990
The drone swarm that performs missions in an autonomous and intelligent overall collaborative manner is an important operational factor in the complex flight space. However, the complex and changing battlefield environment greatly affects the mission execution and survivability of drone swarm. In this paper, we formulated the problem of formation reconfiguration to avoid collision with obstacles with minimum cost in a complex flight space as a two-stage stochastic programming with considering uncertain moving obstacles, where we first calculate the optimal formation parameters of the drone swarm that can avoid certain fixed obstacles with minimum reconfiguration cost in the first-stage. For the uncertain moving obstacles, we calculate the extra cost for avoiding in the second-stage after the distribution of moving obstacles is known, and add this part of the cost to the first-stage to ensure the overall reconfiguration cost is minimized Moreover, the sample average approximation (SAA) method is exploited to solve this stochastic programming problem. Extensive experimental results from the OMNeT++ simulation environment indicate both the feasibility and effectiveness of our proposed two stage stochastic programming approach and the better performs, compared with the existing approach.
The conventional deep learning approaches for solving time-series problem such as long-short term memory (LSTM) and gated recurrent unit (GRU) both consider the time-series data sequence as the input with one single u...
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Often, while modelling complex systems, it is necessary to take into account the impact of random factors on the system as well as the structure of the system itself. If the system has a hierarchical decision-making s...
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This paper presents the stochastic decomposition (SD) algorithms for two classes of stochastic programming problems: (1) two-stage stochastic quadratic-linear programming (SQLP) in which a quadratic program defines th...
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This paper presents the stochastic decomposition (SD) algorithms for two classes of stochastic programming problems: (1) two-stage stochastic quadratic-linear programming (SQLP) in which a quadratic program defines the objective function in the first stage and a linear program defines the value function in the second stage and (2) two-stage stochastic quadratic-quadratic programming (SQQP) which has quadratic programming problems in both stages. Similar to their stochastic linear programming (SLP) predecessor, these iterative schemes in SD approximate the objective function using piecewise affine/quadratic minorants and then apply a stochastic proximal mapping to obtain the next iterate. In this paper we show that under some assumptions, the proximal mapping applied in SD obeys a contraction mapping property even though the approximations are based on sequential random samples. Following that, we demonstrate that under those assumptions, SD can provide a sequence of solutions converging to the optimal solution with a sublinear convergence rate in both SQLP and SQQP problems. Finally, we present an "in-sample" stopping rule to assess the optimality gap by constructing consistent bootstrap estimators.
The work is devoted to the development of a method for solving the stochastic programming problem with a deterministic objective function and individual probabilistic constraints. Each probabilistic constraint is a co...
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In this paper, we consider a newsvendor problem with price elasticity of probabilistic demand. The objective is to determine the optimal price that maximizes profits under various random demand fluctuations by using p...
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Limiting flight delays during operations has become a critical research topic in recent years due to their prohibitive impact on airlines, airports, and passengers. A popular strategy for addressing this problem consi...
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The presence of renewable distributed generation (DG) in electrical distribution systems (EDSs) has been increased in recent years, bringing technical, economical, and environmental benefits. However, the stochastic n...
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In this paper, we apply kernel mean embedding methods to sample-based stochastic optimization and control. Specifically, we use the reduced-set expansion method as a way to discard sampled scenarios. The effect of suc...
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