Description Usage Arguments Value Author(s) References Examples

Computes the weights of assets (relative capital allocation) for a sparse approximation of a financial index.

1 2 | ```
spIndexTrack(X, r, lambda, u = 1, measure = c("ete", "dr", "hete",
"hdr"), hub = NULL, w0 = NULL, thres = 1e-09)
``` |

`X` |
m-by-n matrix of net returns (m samples, n assets). |

`r` |
m dimensional vector of the net returns of the index. |

`lambda` |
sparsity weight factor. Any nonnegative number (suggested range |

`u` |
upper bound of the weights.Default value |

`measure` |
performance measure. Possible values |

`hub` |
Huber parameter. Required if |

`w0` |
initial point. If |

`thres` |
threshold value. All the weights less or equal to |

An n-dimensional vector with allocation weights on the assets.

Konstantinos Benidis and Daniel P. Palomar

K. Benidis, Y. Feng, D. P. Palomar, "Sparse Portfolios for High-Dimensional Financial Index Tracking,"
*IEEE Transactions on Signal Processing*, vol. 66, no. 1, pp. 155-170, Jan. 2018.

1 2 3 4 5 6 7 8 9 10 11 | ```
library(sparseIndexTracking)
library(xts)
# load data
data(INDEX_2010)
# fit portfolio under error measure ETE (Empirical Tracking Error)
w_ete <- spIndexTrack(INDEX_2010$X, INDEX_2010$SP500, lambda = 1e-7, u = 0.5, measure = 'ete')
# show cardinality achieved
cat("Number of assets used:", sum(w_ete > 1e-6))
``` |

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