Generate Synthetic Log-Returns from a Jump-Diffusion Model

Convenience function for generating reproducible synthetic log-returns for use in examples, tests, and vignettes. All package examples and tests use this function to avoid any dependency on live market data.

Usage

jdSampleData(
  model = "merton",
  n = 500,
  mu = 0.05,
  sigma = 0.2,
  lambda = 1,
  mu_j = -0.1,
  sigma_j = 0.15,
  dt = 1/252,
  seed = 42L
)

Arguments

model: Character. Model type: "merton" (default).
n: Integer. Number of log-returns to generate (default 500).
mu: Numeric. Drift (default 0.05).
sigma: Positive numeric. Diffusion vol (default 0.20).
lambda: Non-negative numeric. Jump intensity (default 1).
mu_j: Numeric. Mean log-jump (default -0.10).
sigma_j: Positive numeric. Std dev of log-jumps (default 0.15).
dt: Numeric. Time step (default 1/252).
seed: Integer. Random seed (default 42).

Value

Numeric vector of n log-returns.

Examples

ret <- jdSampleData("merton", n = 200, seed = 42)
head(ret)
#> [1]  0.0198005935  0.0050908610 -0.0134007718 -0.0008211702 -0.0077678841
#> [6]  0.0259456215
hist(ret, breaks = 40, main = "Synthetic Merton Returns")