Threshold Accepting Approach to Improve Bound-based Approximations for Portfolio Optimization
作者： Daniel Kuhn , Panos Parpas , Berç Rustem
DOI: 10.1007/978-3-540-77958-2_1
关键词:
摘要: A discretization scheme for a portfolio selection problem is discussed. The model benchmark relative, mean-variance optimization in continuous time. In order to make the computationally tractable, it discretized time and space. This approximation designed such way that optimal values of approximate problems yield bounds on value original problem. convergence discussed as granularity increased. threshold accepting algorithm attempts find most accurate among all discretizations given complexity also proposed. Promising results numerical case study are provided.
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