| Type: | Package |
| Title: | ARMA Models to Value Stock Options |
| Version: | 1.0.0 |
| Maintainer: | Brian MacCarvill <brianmaccarvills@gmail.com> |
| Description: | Providing ways to estimate the value of European stock options given historical stock price data. It includes functions for calculating option values based on autoregressive–moving-average (ARMA) models and generates information about these models. This package is make to be easy to understand and for financial analysis capabilities. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | forecast, stats |
| RoxygenNote: | 7.3.1 |
| NeedsCompilation: | no |
| Packaged: | 2025-07-07 19:32:26 UTC; brian |
| Author: | Brian MacCarvill [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2025-07-11 12:40:06 UTC |
Call Option Values for Differently Priced European Call Options
Description
This function calculates the value of the a European call option for a list of strike price / buy values, given stock price data and a given future time.
Usage
CallOptionsOverStrikePrices(
stock_data,
future_time,
buy_values,
max.p = 5,
max.q = 5,
method = "CSS-ML"
)
Arguments
stock_data |
Numeric vector of stock prices data. |
future_time |
Numeric constant of the future time |
buy_values |
Numeric vector of the buy values at which to calculate the call option values |
max.p |
The maximum order of the Auto Regressive part of the ARMA model (default is set to 5) |
max.q |
The maximum order of the Moving Average part of the ARMA model (default is set to 5) |
method |
The way that the ARMA model is calculated, accepted values are "ML", "CSS-ML" and "CSS" |
Value
Estimated values of a European call option at different buy values
Examples
library(stats)
library(forecast)
# Create simulated data
n = 100
set.seed(42)
arma_values = arima.sim(n = n, model = list(ar = c(0.6), ma = c(0.5, -0.5)))
linear_model = 5 + 1:n
stock_data = arma_values + linear_model
future_time = 3
buy_values = seq(90, 110, length.out = 5)
CallOptionsOverStrikePrices(stock_data, future_time, buy_values)
Time Sensitivity Analysis for European Call Option
Description
This function calculates the value of the a European call option for a list of future time values, given stock price data and a given buy value.
Usage
CallOptionsOverTime(
stock_data,
future_times,
buy_value,
max.p = 5,
max.q = 5,
method = "CSS-ML"
)
Arguments
stock_data |
Numeric vector of stock prices data. |
future_times |
Numeric vector of the future times |
buy_value |
Numeric value representing the buy value |
max.p |
The maximum order of the autoregressive part of the ARMA model (default is 5). |
max.q |
The maximum order of the moving average part of the ARMA model (default is 5). |
method |
The way that the ARMA model is calculated, accepted values are "ML", "CSS-ML" and "CSS" |
Value
Estimated values of a European call option at different future times
Examples
library(stats)
library(forecast)
# Create simulated data
n = 100
set.seed(42)
arma_values = arima.sim(n = n, model = list(ar = c(0.6), ma = c(0.5, -0.5)))
linear_model = 5 + 1:n
stock_data = arma_values + linear_model
future_times = c(1,3,5)
buy_value = 105
CallOptionsOverTime(stock_data, future_times, buy_value)
Strike Price Sensitivity Analysis for European Put Option
Description
This function calculates the value of the a European put option for a list of strike price / sell values, given stock price data and a given future time.
Usage
PutOptionsOverStrikePrices(
stock_data,
future_time,
sell_values,
max.p = 5,
max.q = 5,
method = "CSS-ML"
)
Arguments
stock_data |
Numeric vector of stock prices data. |
future_time |
Numeric constant of the future time |
sell_values |
Numeric vector of the sell values to calculate the put option values at |
max.p |
The maximum order of the autoregressive part of the ARMA model (default is 5) |
max.q |
The maximum order of the moving average part of the ARMA model (default is 5) |
method |
The way that the ARMA model is calculated, accepted values are "ML", "CSS-ML" and "CSS" |
Value
Estimated values of a European put option at different sell values
Examples
library(stats)
library(forecast)
n = 100
set.seed(42)
arma_values = arima.sim(n = n, model = list(ar = c(0.6), ma = c(0.5, -0.5)))
linear_model = 5 + 1:n
stock_data = arma_values + linear_model
future_time = 2
sell_values = seq(90, 110, length.out = 5)
PutOptionsOverStrikePrices(stock_data, future_time, sell_values)
Time Sensitivity Analysis for European Call Option
Description
This function calculates the value of the a European put option for a list of future time values, given stock price data and a given buy value.
Usage
PutOptionsOverTime(
stock_data,
future_times,
sell_value,
max.p = 5,
max.q = 5,
method = "CSS-ML"
)
Arguments
stock_data |
Numeric vector of stock prices data. |
future_times |
Numeric vector of the future times |
sell_value |
Numeric value representing the sell value |
max.p |
The maximum order of the autoregressive part of the ARMA model (default is 5). |
max.q |
The maximum order of the moving average part of the ARMA model (default is 5). |
method |
The way that the ARMA model is calculated, accepted values are "ML", "CSS-ML" and "CSS" |
Value
Estimated values of a European put option at different future times
Examples
library(stats)
library(forecast)
# Create simulated data
n = 100
set.seed(42)
arma_values = arima.sim(n = n, model = list(ar = c(0.6), ma = c(0.5, -0.5)))
linear_model = 5 + 1:n
stock_data = arma_values + linear_model
sell_value = 110
future_times = c(1, 3, 5)
PutOptionsOverTime(stock_data = stock_data, future_times = future_times, sell_value = sell_value)
Estimate European Call Option Value
Description
This function calculates the value of a European call option based on stock data, a future time value, and a buy value
Usage
europeanCallOptionValue(
stock_data,
future_time,
buy_value,
max.p = 5,
max.q = 5,
method = "CSS-ML"
)
Arguments
stock_data |
Numeric vector of stock prices data |
future_time |
Numeric constant of the future time |
buy_value |
The numeric buy value of the European call option |
max.p |
The maximum order of the autoregressive part of the ARMA model (default is set to 5) |
max.q |
The maximum order of the moving average part of the ARMA model (default is set to 5) |
method |
The way that the ARMA model is calculated, accepted values are "ML", "CSS-ML" and "CSS" |
Value
Estimate the value of a European call option, determine the probability of making profits, and model an appropriate ARMA model for the given stock data
Examples
library(stats)
library(forecast)
# Create simulated data
n = 100
set.seed(42)
arma_values = arima.sim(n = n, model = list(ar = c(0.5), ma = c(0.5, -0.5)))
linear_model = 5 + 1:n
stock_data = arma_values + linear_model
buy_value = 105
future_time = 1
europeanCallOptionValue(stock_data = stock_data, future_time, buy_value, max.p = 5, max.q = 5)
Estimate European Put Option Value
Description
This function calculates the value of a European put option based on stock data, a future time value, and a sell value
Usage
europeanPutOptionValue(
stock_data,
future_time,
sell_value,
max.p = 5,
max.q = 5,
method = "CSS-ML"
)
Arguments
stock_data |
Numeric vector of stock prices data |
future_time |
Numeric constant of the future time |
sell_value |
The numeric sell value of the European put option. |
max.p |
The maximum order of the autoregressive part of the ARMA model (default is set to 5) |
max.q |
The maximum order of the moving average part of the ARMA model (default is set to 5) |
method |
The way that the ARMA model is calculated, accepted values are "ML", "CSS-ML" and "CSS" |
Value
Estimate the value of a European put option, determine the probability of making profits, and model an appropriate ARMA model for the given stock data.
Examples
library(stats)
library(forecast)
# Create simulated data
n = 100
set.seed(42)
arma_values = arima.sim(n = n, model = list(ar = c(0.6), ma = c(0.5, -0.5)))
linear_model = 5 + 1:n
stock_data = arma_values + linear_model
europeanPutOptionValue(stock_data = stock_data,future_time = 5,sell_value = 110,max.p = 5,max.q = 5)