Description Usage Arguments Details Value Author(s) References Examples

This function performs k-geometric means for time-varying value-at-risk.

1 | ```
kgvar(y, centers, iter.max = 10, conf.level = 0.95)
``` |

`y` |
data frame from which the estimate is to be computed; first column corresponds to time and the second to the remainder of interest. |

`centers` |
the number of clusters or a set of initial
(distinct) cluster centres. If a number, a random set of (distinct)
rows in |

`iter.max` |
the maximum number of iterations allowed. The default is 10. |

`conf.level` |
the confidence level. The default is 0.95. |

The intermediate sequence *κ_T* is chosen
proportional to *T/\log T*.

kgvar returns an object of class `"kgvar"`

which has a
fitted method. It is a list with at least the following components:

`var.new` |
cluster center value-at-risk function. |

`clusters` |
cluster allocation. |

`Y` |
raw data. |

`n.clust` |
number of clusters. |

`scale.param` |
the scale parameters in the Pareto-like tail specification. |

`conf.level` |
the confidence level. |

`hill` |
hill estimator of extreme value index. |

The `plot`

method depicts the k-geometric means algorithm for
time-varying value-at-risk. If `c.c`

is `TRUE`

, the method displays the
cluster means.

Miguel de Carvalho, Rodrigo Rubio.

Rubio, R., de Carvalho, M. and Huser, R. (2018) Similarity-Based Clustering of Extreme Losses from the London Stock Exchange. Submitted.

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