helps us to quantify the order of observations required for an accurate estimation. For LTP models with 4 parameters, such as the LTP $t$ and LTP SAS models, it is necessary to have at least $200$ observations in order to accurately estimate the tail parameters. In fact, the proposed flexible models are not recommended with small samples since, intuitively, these do not contain information about the features captured by the shape parameters $\gamma$ and $\delta$.

   Par.                                                                                                                             
  ------ ------ ------------------ -------------- -------------- --------------- ----------- ------------------ ------------------- ---------------
          $n$       $\hat\mu$       $\hat\sigma$   $\hat\gamma$   $\hat\delta$    $\hat\mu$     $\hat\sigma$       $\hat\gamma$      $\hat\delta$
           30         0.0052          -0.0347         0.0018         -2858.8       -0.0205        -0.0350             -0.0197           -2502.9
           50        -0.0026          -0.0269        -0.0017         -623.9        -0.0168        -0.0247             -0.0114           -476.8
          100         0.0005          -0.0096         0.0005          -5.8         -0.0047        -0.0090             -0.0037            27.0
          250         0.0010          -0.0028        -0.0001         -0.0883       0.0008         -0.0035             -0.0007           -0.0890
          500        -0.0001          -0.0003         0.0001         -0.0387       -0.0008        -0.0004             -0.0007           -0.0391
          1000   4$\times10^{-5}$     -0.0001         0.0001         -0.0202       0.0002     5$\times10^{-5}$   -5$\times10^{-5}$      -0.0187
           30         0.3303           0.0867         0.1034      7$\times10^8$    0.3054          0.0874             0.0952         5$\times10^8$
           50         0.1465           0.0493         0.0468      1$\times10^8$    0.1377          0.0490             0.0441         4$\times10^7$
          100         0.0590           0.0215         0.0191      7$\times10^4$    0.0557          0.0214             0.0181         5$\times10^6$
          250         0.0203           0.0081         0.0069         0.1494        0.0195          0.0082             0.0065            0.1505
          500         0.0102           0.0039         0.0034         0.0605        0.0096          0.0039             0.0032            0.0605
          1000        0.0050           0.0019         0.0016         0.0283        0.0047          