[R-SIG-Finance] correlation matrix

debashis dutta dutt.debashis at gmail.com
Sun Nov 27 12:36:03 CET 2011


Dear Enrico & Arun,

Thanks for the help. I will try for the same.

Best regards
Debashis

On 27/11/2011, Arun Kumar Saha <arun.kumar.saha at gmail.com> wrote:
> Thanks Enrico for your pointer. I was not aware of them!
>
> Thanks and regards,
> _____________________________________________________
>
> Arun Kumar Saha, FRM
> QUANTITATIVE RISK AND HEDGE CONSULTING SPECIALIST
> Visit me at: http://in.linkedin.com/in/ArunFRM
> _____________________________________________________
>
>
> On Sun, Nov 27, 2011 at 4:58 PM, Enrico Schumann
> <enricoschumann at yahoo.de>wrote:
>
>>
>>
>> Am 27.11.2011 10:52, schrieb Arun.stat:
>>
>>> Hi Debashis, I dont think there is any direct implementation in R for
>>> that.
>>>
>>
>> Just for the record: see for example function 'nearPD' in package Matrix,
>> or 'repairMatrix' in package NMOF.
>>
>>  What you need is to have a PD matrix for the underlying VCV matrix, which
>>> is
>>> not the case for yours. Do you have lot of missing data? May be you can
>>> try
>>> with constructing VCV matrix after considering pairwise variables and
>>> then
>>> tweak the eigen value little bit to make your estimated VCV matrix PD.
>>>
>>> One algorithm for that may be like (if your estimated matrix is NND):
>>>
>>> Mod_VCV = Original_VCV + (Identity_Mat(n) - Original_VCV)*(Lambda/(1-**
>>> Lambda)
>>> + a_very_small_positive_number)
>>>
>>> Lambda is the smallest eigen value for you estimated VCV matrix.
>>>
>>> HTH,
>>>
>>> Thanks and regards,
>>> ______________________________**_______________________
>>>
>>> Arun Kumar Saha, FRM
>>> QUANTITATIVE RISK AND HEDGE CONSULTING SPECIALIST
>>> Visit me at:
>>> http://in.linkedin.com/in/**ArunFRM<http://in.linkedin.com/in/ArunFRM>
>>> ______________________________**_______________________
>>>
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>> --
>> Enrico Schumann
>> Lucerne, Switzerland
>> http://nmof.net/
>>
>
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-- 
Dr. Debashis Dutta
Risk Managment Professional



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