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correlation matrix
7 messages · Patrick Burns, debashis dutta, Enrico Schumann +1 more
Can you be more specific about what the problem is, and perhaps tell us what you've tried that is unsatisfactory?
On 27/11/2011 09:06, debashis dutta wrote:
Hi, I am generating correlation matrix between macro variables of interest rates and forex rates. Is there any package in R that assists it? Any assistance is highly solicited. Best regards Debashis [[alternative HTML version deleted]]
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Hi Debashis, I dont think there is any direct implementation in R for that. 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 _____________________________________________________ -- View this message in context: http://r.789695.n4.nabble.com/correlation-matrix-tp4112075p4112158.html Sent from the Rmetrics mailing list archive at Nabble.com.
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 _____________________________________________________ -- View this message in context: http://r.789695.n4.nabble.com/correlation-matrix-tp4112075p4112158.html Sent from the Rmetrics mailing list archive at Nabble.com. _______________________________________________ R-SIG-Finance at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
Enrico Schumann Lucerne, Switzerland http://nmof.net/
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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> ______________________________**_______________________ -- View this message in context: http://r.789695.n4.nabble.com/** correlation-matrix-**tp4112075p4112158.html<http://r.789695.n4.nabble.com/correlation-matrix-tp4112075p4112158.html> Sent from the Rmetrics mailing list archive at Nabble.com. ______________________________**_________________ R-SIG-Finance at r-project.org mailing list https://stat.ethz.ch/mailman/**listinfo/r-sig-finance<https://stat.ethz.ch/mailman/listinfo/r-sig-finance> -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go. -- Enrico Schumann Lucerne, Switzerland http://nmof.net/ [[alternative HTML version deleted]] _______________________________________________ R-SIG-Finance at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
Dr. Debashis Dutta Risk Managment Professional