NNS Monte Carlo Sampling — NNS.MC • NNS

Monte Carlo sampling from the maximum entropy bootstrap routine NNS.meboot, ensuring the replicates are sampled from the full [-1,1] correlation space.

Usage

NNS.MC(
  x,
  reps = 30,
  lower_rho = -1,
  upper_rho = 1,
  by = 0.01,
  exp = 1,
  type = "spearman",
  drift = TRUE,
  target_drift = NULL,
  target_drift_scale = NULL,
  xmin = NULL,
  xmax = NULL,
  ...
)

Arguments

x: vector of data.
reps: numeric; number of replicates to generate, 30 default.
lower_rho: numeric [-1,1]; .01 default will set the from argument in seq(from, to, by).
upper_rho: numeric [-1,1]; .01 default will set the to argument in seq(from, to, by).
by: numeric; .01 default will set the by argument in seq(-1, 1, step).
exp: numeric; 1 default will exponentially weight maximum rho value if exp > 1. Shrinks values towards upper_rho.
type: options("spearman", "pearson", "NNScor", "NNSdep"); type = "spearman"(default) dependence metric desired.
drift: logical; drift = TRUE (default) preserves the drift of the original series.
target_drift: numerical; target_drift = NULL (default) Specifies the desired drift when drift = TRUE, i.e. a risk-free rate of return.
target_drift_scale: numerical; instead of calculating a target_drift, provide a scalar to the existing drift when drift = TRUE.
xmin: numeric; the lower limit for the left tail.
xmax: numeric; the upper limit for the right tail.
...: possible additional arguments to be passed to NNS.meboot.

Value

ensemble average observation over all replicates as a vector.
replicates maximum entropy bootstrap replicates as a list for each rho.

References

Vinod, H.D. and Viole, F. (2020) Arbitrary Spearman's Rank Correlations in Maximum Entropy Bootstrap and Improved Monte Carlo Simulations. doi:10.2139/ssrn.3621614

Examples

if (FALSE) { # \dontrun{
# To generate a set of MC sampled time-series to AirPassengers
MC_samples <- NNS.MC(AirPassengers, reps = 10, lower_rho = -1, upper_rho = 1, by = .5, xmin = 0)
} # }