# A Dynamic Structure for High Dimensional Covariance Matrices and its Application in Portfolio Allocation

arXiv: https://arxiv.org/abs/1506.01407 · HTML: https://mlsanity.com/paper/1506.01407/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2015.

## Built on by (most cited first, 1 of 1)

- [High-Dimensional Dynamic Covariance Models with Random Forests](https://mlsanity.com/paper/2505.12444/) (2025)

## Abstract (arXiv)

Estimation of high dimensional covariance matrices is an interesting and important research topic. In this paper, we propose a dynamic structure and develop an estimation procedure for high dimensional covariance matrices. Asymptotic properties are derived to justify the estimation procedure and simulation studies are conducted to demonstrate its performance when the sample size is finite. By exploring a financial application, an empirical study shows that portfolio allocation based on dynamic high dimensional covariance matrices can significantly outperform the market from 1995 to 2014. Our proposed method also outperforms portfolio allocation based on the sample covariance matrix and the portfolio allocation proposed in Fan, Fan and Lv (2008).