sim_simple: Return Simulation from multivariate normal distribution
In FinYang/stocon: Portfolio selection in a Stochastic control setting

Description Usage Arguments Details Value Author(s)

View source: R/simulation.R

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 `par` and `rho_do` will be ignored. If NULL, will be calculated using the correlation function and vol.

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

List of returns

Yangzhuoran Yang

FinYang/stocon documentation built on Oct. 15, 2019, 8:31 p.m.