Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Directional Variance Adjustment: improving covariance estimates for
  high-dimensional portfolio optimization   Robust and reliable covariance estimates play a decisive role in financial
and many other applications. An important class of estimators is based on
Factor models. Here, we show by extensive Monte Carlo simulations that
covariance matrices derived from the statistical Factor Analysis model exhibit
a systematic error, which is similar to the well-known systematic error of the
spectrum of the sample covariance matrix. Moreover, we introduce the
Directional Variance Adjustment (DVA) algorithm, which diminishes the
systematic error. In a thorough empirical study for the US, European, and Hong
Kong market we show that our proposed method leads to improved portfolio
allocation.

29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex