assetsSim              package:fAssets              R Documentation

_S_i_m_u_l_a_t_i_n_g _M_u_l_t_i_v_a_r_i_a_t_e _A_s_s_e_t _S_e_t_s

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

     Simulates multivariate artificial data sets of assets,  from a
     multivariate normal, skew normal, or (skew)  Student-t
     distribution.

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

     assetsSim(n, dim = 2, model = list(mu = rep(0, dim), Omega = diag(dim), 
         alpha = rep(0, dim), df = Inf), assetNames = NULL) 

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

       n: integer value, the number of data records to be simulated.  

     dim: integer value, the dimension (number of columns) of the
          assets  set. 

   model: a list of model parameters: 
           'mu' a vector of mean values, one for each asset series, 
           'Omega' the covariance matrix of assets, 
           'alpha' the skewness vector, and 
           'df' the number of degrees of freedom which is a measure for
          the fatness of the tails (excess kurtosis). 
           For a symmetric distribution 'alpha' is a vector of zeros.
          For the normal distributions 'df' is not used and set to 
          infinity, 'Inf'. Note that all assets have the same value 
          for 'df'. 

assetNames: [assetsSim] - 
           a vector of character strings of length 'dim' allowing for
          modifying the names of the individual assets. 

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

     'assetsSim()'  
      returns a data.frame of simulated assets.

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

     Adelchi Azzalini for R's 'sn' package, 
      Torsten Hothorn for R's 'mtvnorm' package, 
      Diethelm Wuertz for the Rmetrics port.

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

     Azzalini A. (1985); _A Class of Distributions Which Includes the
     Normal Ones_, Scandinavian Journal of Statistics 12, 171-178. 

     Azzalini A. (1986); _Further Results on a Class of Distributions
     Which Includes  the Normal Ones_, Statistica 46, 199-208. 

     Azzalini A., Dalla Valle A. (1996); _The Multivariate Skew-normal
     Distribution_, Biometrika 83, 715-726. 

     Azzalini A., Capitanio A. (1999); _Statistical Applications of the
     Multivariate Skew-normal  Distribution_, Journal Roy. Statist.
     Soc. B61, 579-602. 

     Azzalini A., Capitanio A. (2003); _Distributions Generated by
     Perturbation of Symmetry with  Emphasis on a Multivariate Skew-t
     Distribution_, Journal Roy. Statist. Soc. B65, 367-389. 

     Genz A., Bretz F. (1999); _Numerical Computation of Multivariate
     t-Probabilities with Application to Power Calculation of Multiple
     Contrasts_,  Journal of Statistical Computation and Simulation 63,
     361-378.

     Genz A. (1992); _Numerical Computation of Multivariate Normal
     Probabilities_, Journal of Computational and Graphical Statistics
     1, 141-149.

     Genz A. (1993);  _Comparison of Methods for the Computation of
     Multivariate Normal Probabilities_, Computing Science and
     Statistics 25, 400-405.

     Hothorn T., Bretz F., Genz A. (2001); _On Multivariate t and Gauss
     Probabilities in R_, R News 1/2, 27-29.

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

_S_e_e _A_l_s_o:

     'MultivariateDistribution'.

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

     ## LPP -
        # Percentual Returns:
        LPP = 100 * as.timeSeries(data(LPP2005REC))[, 1:3]
        colnames(LPP)
        
     ## assetsFit -
        # Fit a Skew-Student-t Distribution:
        fit = assetsFit(LPP)
        print(fit)
        # Show Model Slot:
        print(fit@model)
        
     ## assetsSim -
        # Simulate set with same statistical properties:
        set.seed(1953)
        lppSim = assetsSim(n = nrow(LPP), dim = ncol(LPP), model = fit@model)
        colnames(lppSim) <- colnames(LPP)
        rownames(lppSim) <- rownames(LPP)
        head(lppSim)
        head(as.timeSeries(lppSim))

