Fast gradient descent method for Mean-CVaR optimization

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• 作者：Garud Iyengar (1)
  Alfred Ka Chun Ma (2) (3)
  
• 关键词：Conditional value ; at ; risk ; Portfolio optimization
• 刊名：Annals of Operations Research
• 出版年：2013
• 出版时间：May 2013
• 年：2013
• 卷：205
• 期：1
• 页码：203-212
• 全文大小：429KB
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• 作者单位：Garud Iyengar (1)
  Alfred Ka Chun Ma (2) (3)
  
  1. Department of Industrial Engineering and Operations Research, Columbia University, New York, NY, 10027, USA
  2. Department of Mathematics, The Chinese University of Hong Kong, Hong Kong, China
  3. Celestial Asia Securities Holdings, Hong Kong, China
  
• ISSN：1572-9338

文摘

We propose an iterative gradient descent algorithm for solving scenario-based Mean-CVaR portfolio selection problem. The algorithm is fast and does not require any LP solver. It also has efficiency advantage over the LP approach for large scenario size.