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.
|Maintainer||Daniel P. Palomar <[email protected]>|
|License||GPL-3 | file LICENSE|
|URL||https://github.com/dppalomar/sparseIndexTracking https://www.danielppalomar.com https://doi.org/10.1109/TSP.2017.2762286|
|Package repository||View on GitHub|
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