Vast portfolio selection with gross-exposure constraints

Jianqing Fan, Jingjin Zhang, Ke Yu

Research output: Contribution to journalArticlepeer-review

192 Scopus citations


This article introduces the large portfolio selection using gross-exposure constraints. It shows that with gross-exposure constraints, the empirically selected optimal portfolios based on estimated covariance matrices have similar performance to the theoretical optimal ones and there is no error accumulation effect from estimation of vast covariance matrices. This gives theoretical justification to the empirical results by Jagannathan andMa. It also shows that the no-short-sale portfolio can be improved by allowing some short positions. The applications to portfolio selection, tracking, and improvements are also addressed. The utility of our new approach is illustrated by simulation and empirical studies on the 100 Fama-French industrial portfolios and the 600 stocks randomly selected from Russell 3000.

Original languageEnglish (US)
Pages (from-to)592-606
Number of pages15
JournalJournal of the American Statistical Association
Issue number498
StatePublished - 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Mean-variance efficiency
  • Portfolio improvement
  • Portfolio optimization
  • Risk assessment
  • Risk optimization
  • Short-sale constraint


Dive into the research topics of 'Vast portfolio selection with gross-exposure constraints'. Together they form a unique fingerprint.

Cite this