Endogenous Asymmetric Sparse Index Tracking

DOI

10.2991/978-94-6239-787-3_34How to use a DOI?

Keywords

Sparse Index Tracking; Endogenous Sparsity; Oracle Inequality; Asymmetric loss

Abstract

This paper introduces endogenous Asymmetric Sparse Index Tracking (eASIT), a framework for partial replication based on a single economically grounded optimization criterion. The investor minimizes an asymmetric tracking loss—penalizing shortfall more heavily than outperformance—plus an investment penalty that induces sparse, implementable portfolios. The contribution is threefold. First, the asymmetry parameter is not a free tuning constant: we show that loss aversion, ambiguity aversion, and recursive multiple priors all imply the same reduced-form asymmetric objective. Second, eASIT admits a non-asymptotic oracle inequality, a debiased central limit theorem for inference, and a dynamic rebalancing rule that links behavioral asymmetry, friction-based regularization, and portfolio implementation.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Jiyang Tang
PY  - 2026
DA  - 2026/09/29
TI  - Endogenous Asymmetric Sparse Index Tracking
BT  - Proceedings of the 2026 4th International Conference on Management Innovation and Economy Development (MIED 2026)
PB  - Atlantis Press
SP  - 333
EP  - 342
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-787-3_34
DO  - 10.2991/978-94-6239-787-3_34
ID  - Tang2026
ER  -