Hi,
I have a discrete set of data on the returns for 3 indices with 206 data
points. Since the number of points is less it doesnt exact look like a
gaussian distribution.
I wanted to fit the data to a gaussian distribution and have used the
fitdist function and have gotten the plots and the mean and sd estimates
for the gaussian that fits my data.
What I then want to do is to get a U=F(x) where U is the uniform variable
corresponding to the CDF function applied on the fitted theoritical CDF
curve. How can I get that?
Equivalent data that I find in matlab. Here the ksdensity gives an array of
f and xi values and I could use the f values for my usage. But I am trying
to work it out in R. The steps that I am going through in R are below. I
have also attached the input sheet that I am using for the indices. Sorry
in advance, case its a dumb one.
Estimate Density
Generate a sample data set from a mixture of two normal distributions.
rng default % for reproducibility
x = [randn(30,1); 5+randn(30,1)];
Plot the estimated density.
[f,xi] = ksdensity(x);
figure
plot(xi,f);
Steps that I am following.
# Reading and finding the returns for 3 indices.
CDSPrices<-read.csv("CDS.csv")
numRows=nrow(CDSPrices)
CDSReturnsN225=CDSPrices$N225[2:numRows]/CDSPrices$N225[1:numRows-1]-1
CDSReturnsSPX=CDSPrices$SPX[2:numRows]/CDSPrices$SPX[1:numRows-1]-1
CDSReturnsIBOVESPA=CDSPrices$IBOVESPA[2:numRows]/CDSPrices$IBOVESPA[1:numRows-1]-1
CDS_Returns<-cbind(CDSReturnsN225,CDSReturnsSPX,CDSReturnsIBOVESPA)
# Using fitdist to fit a gaussian distribution onto the discrete empirical
data I have.
library(fitdistrplus)
fittedNormal<-fitdist(CDS_Returns[,1],"norm")
plot(fittedNormal)
> fittedNormal[]
$estimate
mean sd
-0.002035951 0.028654032
$method
[1] "mle"
$sd
mean sd
0.001996421 0.001403953
Reference
http://cran.r-project.org/web/packages/fitdistrplus/fitdistrplus.pdf ~
Page 15
--
*Jacob Varughese*
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