fitHeavyTail version 0.2.0 (2023-5-1)

• New methods OPP and POP for the estimation of nu: nu_OPP_estimator() and nu_POP_estimator().

• Function fit_mvt() updated using POP as default method for nu.

• Vignette updated.

• Functions fit_Tyler() and fit_Cauchy() now recover the missing scaling factor with the improved OPP-harmonic method.

• New contributors added for OPP and POP methods: Frederic Pascal and Esa Ollila.

fitHeavyTail version 0.1.3 (2022-4-14)

• New method for skewed t distributions: fit_mvst()

• fit_mvt() and fit_mvst(): Now the bounds for nu estimation can be set as a global option, e.g.: options(nu_min = 4.2).

• Fixed description regarding covariance matrix for Cauchy distribution.

• fit_mvt(): It accepts weights as argument to weight differently the samples (as opposed to uniform weights).

• fit_mvt(): Many more methods to estimate nu iteratively (via argument nu_iterative_method).

• fit_mvt(): New argument scale_minMSE to include a correction factor in the covariance matrix for minimum MSE (still in development).

fitHeavyTail version 0.1.2 (2020-1-7)

• Vignette revised: detailed descriptions of the algorithms included.

• Comparison with additional existing benchmark sn::selm() included in the vignette.

• Now the three fitting functions also return the number of iterations and elapsed cpu_time.

• Significant revision of the fitting function fit_mvt(); in particular:

  • the nu_target for the estimation of nu has been removed since it was not effective;
  • several new options for the initial value of nu or fixed value of nu have been included; and
  • improved and more robust estimation of nu at each EM iteration.

• Function fit_mvt() now allows the choice (via the argument na_rm) to drop the observations with NAs
  or impute them.

Initial release of portfolioBacktest version 0.1.1 (2019-11-22)

• Initial release is on CRAN.

• It includes three functions for heavy tails fitting: fit_mvt(), fit_Tyler(), and fit_Cauchy().

• Vignette illustrates its use and comparison with existing packages.

• Tests are included.

• fit_mvt() can deal with NAs and a factor model structure on the covariance matrix.