BS_fit_rolling

0th

Percentile

Fit Black-Scholes Parameters Over Rolling Window

Function to estimate the volatility, $\sigma$, and drift, $\mu$. E.g., the window can be over a given number of months. See vignette("Distance-to-default", package = "DtD") for details.

Usage
BS_fit_rolling(S, D, T., r, time, dt, vol_start, method = c("iterative",
"mle"), tol = 1e-12, eps = 1e-08, grp, width, min_obs)
Arguments
S

numeric vector with observed stock prices.

D

numeric vector or scalar with debt due in T..

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_start

numeric scalar with starting value for $\sigma$.

method

string to specify which estimation method to use.

tol

numeric scalar with tolerance to get_underlying. The difference is scaled if the absolute of S is large than tol as in the tolerance argument to all.equal.numeric.

eps

numeric scalar with convergence threshold.

grp

integer vector with the group identifier (e.g., units of months).

width

integer scalar with the units of grp to include in the rolling window.

min_obs

integer scalar for the minimum number of observation required in each window.

Value

Matrix with the grp, number of observation in the window, parameter estimates, and 'n_iter' as in BS_fit, and whether the estimation method was successful.

An error attribute is added in case other code than optim fails. It is a list of lists with the grp index where the method failed and the output from try.

BS_fit

Aliases
• BS_fit_rolling
Examples
# NOT RUN {
# Simulate data
set.seed(55770945)
n <- 21L * 3L * 12L # 21 trading days for 3 years w/ 12 months
sims <- BS_sim(
vol = .1, mu = .05, dt = .1, V_0 = 100, T. = 1,
D = runif(n, 80, 90), r = runif(n, 0, .01))
sims$month <- (1:nrow(sims) - 1L) %/% 21L + 1L # throw out some months sims <- subset(sims, !month %in% 15:24) # assign parameters grp <- sims$month
width <- 12L        # window w/ 12 month width
min_obs <- 21L * 3L # require 3 months of data

# estimate results with R loop which is slightly simpler then the
# implementation
grps <- unique(grp)
out <- matrix(
NA_real_, nrow = length(grps), ncol = 6,
dimnames = list(NULL, c("mu", "vol", "n_iter", "success", "n_obs", "grp")))
for(g in grps){
idx <- which(grps == g)
keep <- which(grp %in% (g - width + 1L):g)
out[idx, c("n_obs", "grp")] <- c(length(keep), g)
if(length(keep) < min_obs)
next
res <- with(
sims[keep, ],
BS_fit(S = S, D = D, T. = T, r = r, time = time, method = "iterative",
vol_start = 1))
out[idx, c("mu", "vol", "n_iter", "success")] <- rep(
do.call(c, res[c("ests", "n_iter", "success")]), each = length(idx))
}

# we get the same with the R function
out_func <- with(sims, BS_fit_rolling(
S = S, D = D, T. = T, r = r, time = time, method = "iterative",
grp = month, width = width, min_obs = min_obs))

all.equal(out[, names(out) != "n_iter"],
out_func[, names(out_func) != "n_iter"])

# }

Documentation reproduced from package DtD, version 0.2.1, License: GPL-2

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