Maximises the log-likelihood of the Merton (1976) model using L-BFGS-B optimisation. Returns a JDFitResult object containing parameter estimates, standard errors, and convergence info.
Usage
fitMerton(
log_returns,
start = c(mu = 0.05, sigma = 0.2, lambda = 1, mu_j = -0.1, sigma_j = 0.15),
dt = 1/252,
N_max = 50L,
verbose = FALSE
)Arguments
- log_returns
Numeric vector of observed log asset returns.
- start
Named numeric vector of starting values. Defaults to
c(mu=0.05, sigma=0.20, lambda=1, mu_j=-0.10, sigma_j=0.15).- dt
Numeric. Time step length (default 1/252).
- N_max
Integer. Mixture truncation (default 50).
- verbose
Logical. Print progress? (default FALSE).
Value
A JDFitResult object.
Examples
# \donttest{
ret <- jdSampleData("merton", n = 500, seed = 42)
fit <- fitMerton(ret, verbose = TRUE)
#> Converged.
print(fit)
#> Merton MLE Fit Result
#> ---------------------
#> Converged : TRUE
#> Log-lik : 1464.8117
#> Estimates (SE):
#> mu : 0.0889 ( NaN)
#> sigma : 0.2022 ( NaN)
#> lambda : 0.5040 ( NaN)
#> mu_j : 0.0801 ( NaN)
#> sigma_j : 0.0000 ( NaN)
confint(fit)
#> 2.5 % 97.5 %
#> mu NaN NaN
#> sigma NaN NaN
#> lambda NaN NaN
#> mu_j NaN NaN
#> sigma_j NaN NaN
# }