ºÝºÝߣshows by User: IgorHlivka / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: IgorHlivka / Wed, 15 Oct 2014 08:21:32 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: IgorHlivka Principal component analysis - application in finance /IgorHlivka/principal-component-analysis-40300329 principalcomponentanalysiswithmathematica10-141015082133-conversion-gate01
Principal component analysis is a useful multivariate times series method to examine and study the drivers of the changes in the entire dataset. The main advantage of PCA is the reduction of dimensionality where the large sets of data get transformed into few principal factors that explain majority of variability in that group. PCA has found many applications in finance – both in risk and yield curve analytics]]>
Principal component analysis is a useful multivariate times series method to examine and study the drivers of the changes in the entire dataset. The main advantage of PCA is the reduction of dimensionality where the large sets of data get transformed into few principal factors that explain majority of variability in that group. PCA has found many applications in finance – both in risk and yield curve analytics]]> Wed, 15 Oct 2014 08:21:32 GMT /IgorHlivka/principal-component-analysis-40300329 IgorHlivka@slideshare.net(IgorHlivka) Principal component analysis - application in finance IgorHlivka Principal component analysis is a useful multivariate times series method to examine and study the drivers of the changes in the entire dataset. The main advantage of PCA is the reduction of dimensionality where the large sets of data get transformed into few principal factors that explain majority of variability in that group. PCA has found many applications in finance – both in risk and yield curve analytics <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/principalcomponentanalysiswithmathematica10-141015082133-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Principal component analysis is a useful multivariate times series method to examine and study the drivers of the changes in the entire dataset. The main advantage of PCA is the reduction of dimensionality where the large sets of data get transformed into few principal factors that explain majority of variability in that group. PCA has found many applications in finance – both in risk and yield curve analytics
Principal component analysis - application in finance from Igor Hlivka
]]> 1604 2 https://cdn.slidesharecdn.com/ss_thumbnails/principalcomponentanalysiswithmathematica10-141015082133-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png Leader of the Quantitative Solutions Group – a dedicated team of experienced quantitative analysts, skilful derivative experts and first-class modellers with extensive expertise in all asset classes and long-term presence in Asian, American and European markets. The team designs, builds and develops pricing models, curves, trading risk tools and live monitoring applications that enable traders to make quick and correct decisions in real-time. Derivatives expert with international experience and expertise in the management, development, and successful implementation of pricing tools, trading analytics and risk routines for rates, credit, commodity and equity products. Lead develope...