Nonnegative-Lasso and application in index tracking. (English) Zbl 1471.62220

Wu, Lan; Yang, Yuehan; Liu, Hanzhong

Comput. Stat. Data Anal. 70, 116-126 (2014).

Summary: This paper proposes the nonnegative-lasso method for variable selection in high dimensional sparse linear regression models with the nonnegative constraints on the coefficients. This method is an extension of Lasso and is shown to have variable selection consistency and estimation consistency under certain condition similar to Irrepresentable Condition in Lasso. To get the solution of the nonnegative-lasso, many algorithms such as Lars, coordinate decent can be used, among which multiplicative updates approach is preferred since it is faster and simpler. The constrained index tracking problem in stock market without short sales is studied in the latter part. The tracking results indicate that nonnegative-lasso can get small tracking error and is successful in assets selection.

MSC:

62-08	Computational methods for problems pertaining to statistics
62J07	Ridge regression; shrinkage estimators (Lasso)
62P05	Applications of statistics to actuarial sciences and financial mathematics

Keywords:

index tracking; nonnegative-Lasso; variable selection consistency; multiplicative updates

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