Description Usage Arguments See Also Examples

Computes the log-likelihood for a given values of *μ* and
*σ*.

1 |

`S` |
numeric vector with observed stock prices. |

`D` |
numeric vector or scalar with debt due in |

`T.` |
numeric vector or scalar with time to maturity. |

`r` |
numeric vector or scalar with risk free rates. |

`time` |
numeric vector with the observation times. |

`dt` |
numeric scalar with time increments between observations. |

`vol` |
numeric scalar with the |

`mu` |
numeric scalar with the |

`tol` |
numeric scalar with tolerance to |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
# we get the same if we call `optim` as follows. The former is faster and is
# recommended
set.seed(4648394)
sims <- BS_sim(
vol = .1, mu = .05, dt = .1, V_0 = 100, T. = 1, D = rep(80, 20), r = .01)
r1 <- with(
sims, BS_fit(S = S, D = D, T. = T, r = r, time = time, method = "mle",
eps = 1e-8, vol_start = .2))
r2 <- optim(c(mu = 0, log_vol = log(.2)), function(par)
-with(
sims, merton_ll(S = S, D = D, T. = T, r = r, time = time,
mu = par["mu"], vol = exp(par["log_vol"]))))
all.equal(r1$n_iter, unname(r2$counts[1]))
all.equal(r1$ests[1], r2$par[1])
all.equal(r1$ests[2], exp(r2$par[2]), check.attributes = FALSE)
# the log-likelihood integrates to one as it should though likely not the
# most stable way to test this
ll <- integrate(
function(x) sapply(x, function(S)
exp(merton_ll(
S = c(1, S), D = .8, T. = 3, r = .01, dt = 1/250, vol = .2,
mu = .05))),
lower = 1e-4, upper = 6)
stopifnot(isTRUE(all.equal(ll$value, 1, tolerance = 1e-5)))
``` |

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