A kind of new time-weighted nonnegative lasso index-tracking model and its application

Qi-an Chen, Qingyu Hu, Hu Yang and Kai Qi

The North American Journal of Economics and Finance, 2022, vol. 59, issue C

Abstract: This study describes improved index-tracking methods to replicate the target index’s market performance in a high-dimensional sparse linear regression with nonnegative constraints on the coefficients. The main objective of this study is to construct a sparse portfolio with a better prediction effect and robustness. Considering the influence of time factors on index tracking, we propose a time-weighted nonnegative lasso index tracking model under different market constraints and define two new time-weighted construction methods. This index tracking model is an extension of Lasso and has variable selection consistency and estimation consistency under time-weighted nonnegative irrepresentable conditions similar to the irrepresentable condition in Lasso. We use the multiplicative updates algorithm to obtain the model’s solution since it is faster and simpler. The constrained index tracking problem in the stock market without short sales is studied in the latter part. The empirical results indicate that the optimized time-weighted nonnegative lasso index tracking model can obtain a smaller out-of-sample tracking error. The constructed portfolio has a better prediction effect and robustness, and we find that the exponential time-weighted method is better than the linear time-weighted method in capturing time information.

Keywords: Portfolio; Index tracking; Time-weighted; Nonnegative lasso; Variable selection (search for similar items in EconPapers)
JEL-codes: C51 C52 G11 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:59:y:2022:i:c:s106294082100200x

DOI: 10.1016/j.najef.2021.101603

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