assetsMeanCov            package:fAssets            R Documentation

_E_s_t_i_m_a_t_i_o_n _o_f _M_e_a_n _a_n_d _C_o_v_a_r_i_a_n_c_e_s _o_f _A_s_s_e_t _S_e_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Estimates the mean and/or covariance matrix of a  time series of
     assets by traditional and robust methods.

_U_s_a_g_e:

     assetsMeanCov(x, 
         method = c("cov", "mve", "mcd", "MCD", "OGK", "nnve", "shrink", "bagged"), 
         check = TRUE, force = TRUE, baggedR = 100, sigmamu = scaleTau2, 
         alpha = 1/2, ...)
         
     getCenterRob(object)
     getCovRob(object)

_A_r_g_u_m_e_n_t_s:

       x: any rectangular time series object which can be converted by
          the  function 'as.matrix()' into a matrix object, e.g. like
          an  object of class 'timeSeries', 'data.frame', or 'mts'.  

  method: a character string, whicht determines how to compute the
          covariance matix. If 'method="cov"' is selected then the
          standard  covariance will be computed by R's base function
          'cov', if  'method="shrink"' is selected then the covariance
          will be computed using the shrinkage approach as suggested in
          Schaefer and Strimmer [2005], if 'method="bagged"' is
          selected then the  covariance will be calculated from the
          bootstrap aggregated (bagged) version of the covariance
          estimator. 

   check: a logical flag. Should the covariance matrix be tested to be
          positive definite? By default 'TRUE'. 

   force: a logical flag. Should the covariance matrix be forced to be
          positive definite? By default 'TRUE'. 

 baggedR: when 'methode="bagged"', an integer value, the number of 
          bootstrap replicates, by default 100. 

 sigmamu: when 'methode="OGK"', a function that computes univariate
          robust  location and scale estimates. By default it should
          return a single  numeric value containing the robust scale
          (standard deviation)  estimate. When 'mu.too' is true (the
          default), 'sigmamu()'  should return a numeric vector of
          length 2 containing robust location  and scale estimates. See
          'scaleTau2', 's_Qn', 's_Sn',  's_mad' or 's_IQR' for examples
          to be used as 'sigmamu'  argument.  For details we refer to
          the help pages of the R-package 'robustbase'. 

  object: a list as returned by the function 'assetsMeanCov'. 

   alpha: when 'methode="MCD"', a numeric parameter controlling the
          size  of the subsets over which the determinant is minimized,
          i.e.,  'alpha*n' observations are used for computing the
          determinant.  Allowed values are between 0.5 and 1 and the
          default is 0.5. For details we refer to the help pages of the
          R-package 'robustbase'. 

     ...: optional arguments to be passed to the underlying estimators.
           For details we refer to the manual pages of the functions 
          'cov.rob' for arguments '"mve"' and '"mcd"' in  the R package
          'MASS', to the functions 'covMcd' and 'covOGK' in the R
          package 'robustbase'. 

_V_a_l_u_e:

     'assetsMeanCov' returns a list with for entries named 'center'
     'cov', 'mu' and 'Sigma'. The list may have a character vector 
     attributed with additional control parameters.

     'getCenterRob' extracts the center from an object as returned by
     the function 'assetsMeanCov'.

     'getCovRob' extracts the covariance from an object as returned by
     the function 'assetsMeanCov'.

_A_u_t_h_o_r(_s):

     Juliane Schaefer and Korbinian Strimmer for R's 'corpcov' package, 
      Diethelm Wuertz for the Rmetrics port.

_R_e_f_e_r_e_n_c_e_s:

     Breiman L. (1996);  _Bagging Predictors_, Machine Learning 24,
     123-140.

     Ledoit O., Wolf. M. (2003); _ImprovedEestimation of the Covariance
     Matrix of Stock Returns  with an Application to Portfolio
     Selection_, Journal of Empirical Finance 10, 503-621. 

     Schaefer J., Strimmer K. (2005);   _A Shrinkage Approach to
     Large-Scale Covariance Estimation and Implications for Functional
     Genomics_, Statist. Appl. Genet. Mol. Biol. 4, 32.

     Wuertz, D., Chalabi, Y., Chen W., Ellis A. (2009); _Portfolio
     Optimization with R/Rmetrics_,  Rmetrics eBook, Rmetrics
     Association and Finance Online, Zurich.

_E_x_a_m_p_l_e_s:

     ## LPP -
        LPP = as.timeSeries(data(LPP2005REC))[, 1:6]
        colnames(LPP)
        
     ## Sample Covariance Estimation:
        assetsMeanCov(LPP)
        
     ## Shrinked Estimation:
        shrink = assetsMeanCov(LPP, "shrink")
        shrink
        
     ## Extract Covariance Matrix:
        getCovRob(shrink)

