Simulate self-independent returns using Choleski Decomposition where correlation depends on the order of assets from multvariate normal distribution
sim_simple(dis_par = list(mu = 0.05, vol = 0.02, df = 2), distribution = c("norm", "t"), Tn = 10, N = 5, M = 10000, varcov = NULL, par = 0.2, rho_do = NULL, dependent = c(NA, "AR1"), den_par = list(AR1 = list(beta_0 = 0.05, beta_1 = 0.5)), return_varcov = FALSE, plus_one = TRUE)
| Tn | Number of periods, excluding time 0. |
|---|---|
| N | Number of assets |
| M | Number of realization |
| par | The parameter in the default correlation function. See Details. |
| rho_do | The function of correlation between assets. See below for the default function. |
| dependent | If the following simulated series are added on the basis of existing ones. |
| mu | Either a scalar or a vector with length N contains mean of the assets returns |
| vol | Either a scalar or a vector with length N contains volatilities of the assets returns |
| varcor | The variance covariance matrix of the assets. Diagonal elements must equal to vol squared.
If supplied, arguments |
List of returns
The correlation function determines the correlation between asset by the difference between the index of the assets.
The default function is rho = exp(-par * |distance|).
User defined correlation function need to have two arguments i and j to indicate the position of assets