Assignment question 1

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Assignment question 1

Bericht  Admin op vr dec 09, 2011 1:33 am

For part 1, anyone else who has an SCR of about 22.9 M with a stdev of about 300k ?
I assume he sent the same data set to everyone, mine is called 'group assignment 1A.xls'.

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Re: Assignment question 1

Bericht  Admin op vr dec 16, 2011 2:44 pm

Rumor has it some other group has approx the same values, but they are too chicken to post it :-).

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Re: Assignment question 1

Bericht  Ward op zo dec 18, 2011 11:30 am

this is our confidence interval:

> ConfidenceInterval
[1] 22467877 23304912

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Re: Assignment question 1

Bericht  Admin op ma dec 19, 2011 11:52 pm

About this first question, two things:

1) I believe it makes more sense to take a one sided confidence interval (I'd say you want to be x% sure that your SCR is high enough, not x% sure that it is between some two values, no?) I admit that it's a matter of taste...

2) Apparently you can't apply the CLT on quantiles like the VaR, i.e. the VaRs you obtained are not normally distributed.
What I believe you can do is use a binomial distribution to obtain a confidence interval. It's explained here:
http://turing.une.edu.au/~stat354/notes/node72.html

Because I'm way too kind: this is how it's implemented it in R:
calculate_conf_limit_VaR<-function(x,p,P){
x <- sort(x)
n <- length(x)
r <- max(which(pbinom((0:n),n,1-p) < (1-P)))
x[r]
}

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Re: Assignment question 1

Bericht  Ward op di dec 20, 2011 12:20 pm

Gil, prof. Hoedemakers heeft in de les gezegd als hint te moeten gebruiken: de CLT. Dan denken wij wel dat we mogen uitgaan van normaal verdeelde VaR

Admin schreef:About this first question, two things:

1) I believe it makes more sense to take a one sided confidence interval (I'd say you want to be x% sure that your SCR is high enough, not x% sure that it is between some two values, no?) I admit that it's a matter of taste...

2) Apparently you can't apply the CLT on quantiles like the VaR, i.e. the VaRs you obtained are not normally distributed.
What I believe you can do is use a binomial distribution to obtain a confidence interval. It's explained here:
http://turing.une.edu.au/~stat354/notes/node72.html

Because I'm way too kind: this is how it's implemented it in R:
calculate_conf_limit_VaR<-function(x,p,P){
x <- sort(x)
n <- length(x)
r <- max(which(pbinom((0:n),n,1-p) < (1-P)))
x[r]
}

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Re: Assignment question 1

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