frontierPoints          package:fPortfolio          R Documentation

_G_e_t _F_r_o_n_t_i_e_r _P_o_i_n_t_s

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

     Extracts the risk and return coordinates of the efficient
     frontier.

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

     frontierPoints(object, frontier = c("both", "lower", "upper"), 
         return = c("mean", "mu"), risk = c("Cov", "Sigma", "CVaR", "VaR"),
         auto = TRUE)

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

  object: an object of class 'fPORTFOLIO'. 

frontier: a character string denoting which part of the efficient
          portfolio should be extractacted. 

  return: character strings denoting which return measure should be
          plotted. Allowed values for the return are either '"mean"',
          or '"mu"'. 

    risk: character strings denoting which risk measure should be
          plotted. Allowed values for the risk measure are either
          '"cov"', '"sigma"',   '"VaR"', or '"CVaR"'. 

    auto: a logical flag. If 'auto' is 'TRUE', the  default setting,
          then the risk willbe identified automatically from the
          object. 

_D_e_t_a_i_l_s:

     The automated risk detection, 'auto=TRUE' takes the  following
     decision:


         if (auto) {
             Type = getType(object)
             Estimator = getEstimator(object)
             if (Type == "MV") risk = "cov"
             if (Type == "MV" & Estimator != "covEstimator") risk =
     "sigma"
             if (Type == "QLPM") risk = "sigma"
             if (Type == "CVaR") risk = "CVaR" 
         }


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

     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:

     ## data -
        Data = SMALLCAP.RET
        Data = Data[, c("BKE", "GG", "GYMB", "KRON")]
        Data

     ## portfolioFrontier -  
        Frontier = portfolioFrontier(Data) 
        
     ## frontierPoints - 
        x = frontierPoints(Frontier, risk = "VaR", auto = FALSE)
        x = frontierPoints(Frontier, risk = "CVaR", auto = FALSE)

