dppalomar/sparseIndexTracking: Design of Portfolio of Stocks to Track an Index

Computation of sparse portfolios for financial index tracking, i.e., joint selection of a subset of the assets that compose the index and computation of their relative weights (capital allocation). The level of sparsity of the portfolios, i.e., the number of selected assets, is controlled through a regularization parameter. Different tracking measures are available, namely, the empirical tracking error (ETE), downside risk (DR), Huber empirical tracking error (HETE), and Huber downside risk (HDR). See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1, pp. 155-170, Jan. 2018. <doi:10.1109/TSP.2017.2762286>.

Getting started

Package details

MaintainerDaniel P. Palomar <[email protected]>
LicenseGPL-3 | file LICENSE
URL https://github.com/dppalomar/sparseIndexTracking https://www.danielppalomar.com https://doi.org/10.1109/TSP.2017.2762286
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
dppalomar/sparseIndexTracking documentation built on Sept. 2, 2018, 6:18 p.m.