All,

I have carved on all methods in MultivariateMoments.R so I can all them

directly

I am working on the modified var calculations. I ran the mVaR.MM on my

data and the results were odd. I reran setting skewness and kurtosis to

0 to compare with GVAR.MM. Still questions. I have the following example

#test for sigfinance

w = 1000000;

Mean = 0.0001898251;

Stdev = 0.01612464;

ExKurtosis = 3.946156;

Skewness = -0.1373454;

GVaR = GVaR.MM(w,Mean,Stdev, .95)

GVaR

MVaR = mVaR.MM(w,Mean,Stdev, 0,0, .95);

#shoud be equal to GVaR

GVaR==MVaR

MVaR

mVaR.MM does not return the GVaR.MM. I found the exkurt was not zero as

I think it should be. I remove the - 3 from that line and exkurt became

zero and mVaR.MM == GVaR results. I have included this modified version

of mVaR.MM below and continued the test. Is this an issue or am I

missing something?

#corrected exkurt calc

mVaR.MM1 = function(w, mu, sigma, M3, M4, p ){

skew = skewness.MM(w,sigma,M3);

exkurt = kurtosis.MM(w,sigma,M4); #removed -3

z = qnorm(1-p);

zc = z + (1/6)*(z^2 -1)*skew

Zcf = zc + (1/24)*(z^3 - 3*z)*exkurt - (1/36)*(2*z^3 - 5*z)*skew^2;

return ( -multivariate_mean(w,mu) - Zcf*StdDev.MM(w,sigma) )

}

#call revised mVAR.MM with m3 and m4 equal 0

MVaR1 = mVaR.MM1(w,Mean,Stdev, 0,0, .95);

#shoud be equal to GVaR

GVaR==MVaR1

MVaR1

#with m3 and m4 not zero

MVaR1 = mVaR.MM1(1, Mean, Stdev, Skewness, ExKurtosis, .95);

MVaR1

MVaR1 = mVaR.MM1(w, Mean, Stdev, Skewness, ExKurtosis, .95);

MVaR1

This result still looks strange and would appreciate any thoughts, with

1 or w weights, I get the same just scaled. Note this is real commodity

data with all statistics generated by table.Stats.

Thanks

Joe

--

*Joe W. Byers*

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