Use “c” when you want to use it a signal in black scholes formula.
read.table function will be used to get the data into R (/\ used opposite
than the one used in windows)
http://www.ats.ucla.edu/stat/r/faq/inputdata_R.htm.
Example 1
> test.txt <- read.table ("C:/Documents and Settings/shivbhakta.joshi/My
Documents/test.txt", header=T)
> print(test.txt)
> column <- test.txt[,c('make','price')].
Example 2
grb <- read.table ("C:/Documents and Settings/shivbhakta.joshi/My
Documents/GRB_afterglow.dat", header=T, skip=1)
x <- (grb[,1]).
Grabbing data from tables into a single dimensional array
x <- (grb[,1])
y <- log(grb[,1]).
Another way to create returns for stock daily return is
f=dft$age/dft$price (using dollar sign extraction)
Deleting column http://www.sr.bham.ac.uk/~ajrs/R/r-manipulate_data.html
> add <- add[-2] to delete the element 2nd
Adding data to array busing column command
> add <- c(1,d).
Using Data frames
Data.frame is used to combine the arrays back into table format
people = data.frame (x,y)
Write.CSV command executed
write.csv(people,"filename.csv")
grb <- read.table ("C:/Documents and Settings/satyadhar.joshi/My
Documents/GRB_afterglow.dat", header=T, skip=1)
x <- (grb[,1])
y <- log(grb[,1])
people = data.frame (x,y)
write.csv(people,"filename.csv")
These methods can be used to compute beta from columns of a csv file.
Working formula of Black Scholes
BS <-
function(S, K, T, r, sig, type="C"){
d1 <- (log(S/K) + (r + sig^2/2)*T) / (sig*sqrt(T))
d2 <- d1 - sig*sqrt(T)
if(type=="C"){
value <- S*pnorm(d1) - K*exp(-r*T)*pnorm(d2)
}
if(type=="P"){
value <- K*exp(-r*T)*pnorm(-d2) - S*pnorm(-d1)
}
return(value)
}
Black Scholes <- function(s, k, r=.1, t=5, sigma=.9,call=TRUE) {
#calculate call/put option
d1 <- (log(s/k)+(r+sigma^2/2)*t)/(sigma*sqrt(t))
d2 <- d1 - sigma * sqrt(t)
ifelse(call==TRUE,s*pnorm(d1) - k*exp(-r*t)*pnorm(d2),k*exp(-r*t)
* pnorm(-d2) - s*pnorm(-d1))
}
Predefined Black schools model:
http://shafik.net/~shafik/FinancialEngineering/Code/
black_scholes ("c",5600,5600,.1,.08,.22 )
this depicts use of functions in R.
Merton Distance to Default (different as per needs)
ARIMA and Forecasting Using R
Commands:
library(Quandl)
> Quandl.auth("1LkmpypqJskJzKcpd2TV")
>stockData=Quandl("YAHOO/INDEX_GSPC", start_date="2004-01-01", end_date="2014-04-17")
stockData[,7]
// ran Arima here:
results2=arima(stockData[,7], order = c(3,0,0))
fit <- arima(myts, order=c(p, d, q)
fit <- arima(stockData[,7],order = c(3,0,0))
results=arima(stockData[,7])
fit <- arima(stockData[,7],order = c(3,0,0))
predict(arima(stockData[,7], order = c(3,0,0))
results=arima(stockData[,7])
x2=(forecast(fit, 100))
plot(x2).
Excel and R:
http://rcom.univie.ac.at/download.html#statconnDCOM
http://www.r-bloggers.com/a-million-ways-to-connect-r-and-excel/
http://cran.r-project.org/web/packages/XLConnect/vignettes/XLConnect.pdf
http://cran.r-project.org/web/packages/XLConnect/XLConnect.pdf
https://stat.ethz.ch/R-manual/R-patched/library/base/html/getwd.html
Playing with arrays and matrix
http://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-do-my-matrices-lose-dimensions_003f
https://stat.ethz.ch/pipermail/r-help/2008-February/154088.html
http://stat.ethz.ch/R-manual/R-devel/library/base/html/rev.html.
ARIMA and Predict statement
http://stat.ethz.ch/R-manual/R-patched/library/stats/html/predict.arima.html
http://people.duke.edu/~rnau/411arim.htm
http://www.statmethods.net/advstats/timeseries.html
http://stackoverflow.com/questions/14272937/time-series-prediction-using-r
http://www.inside-r.org/packages/cran/forecast/docs/forecast.Arima.
Market check
http://www.bloomberg.com/quote/SPX:IND/chart.
Other good References
http://heather.cs.ucdavis.edu/~matloff/132/NSPpart.pdf.
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