black76ImVoDF: black76ImVoDF

In OUKUN0705/black76IV: Black76 model

Description

black76ImVoDF applies black76ImVo on each row of a dataframe with option price, strike price, future price, option type, interest rate, and time to maturity as columns.

Usage

black76ImVoDF(df, Ftdf, Kdf, typedf, Timedf, rdf, optionPricedf,
  sigma_ini = NULL, tol = 1e-10, maxiter = 1000, output = c("dataframe",
  "IV"))

Arguments

df	A dataframe.
Ftdf	Name of a columns in the dataframe corresponding to future price.
Kdf	Name of a columns in the dataframe corresponding to strike price.
typedf	Name of a columns in the dataframe corresponding to option type.
Timedf	Name of a columns in the dataframe corresponding to time to maturity.
rdf	Name of a columns in the dataframe corresponding to interest rate.
optionPricedf	Name of a columns in the dataframe corresponding to option price.
sigma_ini	Initial value for iteration in the Newton-Raphson method. Numeric object or NULL (default). If no value is provided, the sigma which maximizes Vega is choosen.
tol	Error tolerance of the interation step.
maxiter	Maximum number of iteration
output	Argument specifying whether the output is the implied volatility calculated only or implied volatility as a column in the dataframe (default).

Value

Implied volatility calculated in a vector or as a column in the dataframe (default).

Examples

Ft <- 50
Kvec <- 20:80
Kvec <- 20:80 + floor(runif(n = 61, min = 0, max = 5))
Timevec <- c(c(3, 6, 9)/12, 1:5)
df0 <- data.frame(expand.grid(Kvec, Timevec))

names(df0) <- c('Strike', 'Time')

r <- (log(df0$Time) + 2) / 100
Vol <- (abs(df0$Strike - 50) ^ (1.3) + 50 + 3/df0$Strike) / 200
type <- sample(x = c("C", "P"), size = 488, replace = TRUE, prob = c(0.5, 0.5))
df <- data.frame(df0, Ft, r, Vol, type)
df2 <- black76DF(df = df, Ftdf = 'Ft', sigmadf = 'Vol', Kdf = 'Strike',
                 typedf = 'type', rdf = 'r', Timedf = 'Time')
head(df2)
df3 <- black76ImVoDF(df = df2, Ftdf = 'Ft', Kdf = 'Strike', typedf = 'type',
                     Timedf = 'Time', rdf = 'r', optionPricedf = 'optionPrice',
                     tol = 1e-10, maxiter = 2000)
summary(abs(df3$Vol - df3$IV))
plot(density(df3$Vol - df3$IV))
df3[which.max(abs(df3$Vol - df3$IV)),]
df3[is.na(df3$IV),]
all(abs(df3$Vol - df3$IV) < 1e-10)

OUKUN0705/black76IV documentation built on May 7, 2019, 8:55 p.m.