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Financial Data


 
It may be tempting to try to apply standard linear regression methods to the analysis of financial data.  However, this may often not work as well as expected.  This is because financial data often has more volatility than other sources of data.  Whichever statistical model is used, they all have assumptions behind the model that need verification to assess the validity of the model and its results.

When using any model (for instance, Black-Scholes), be sure to know the assumptions behind the model, and to check the assumptions when using the model.  If the assumptions behind a statistical model are not met, then this casts doubt on the validity of the model and any conclusions.


A good reference on applying statistical methods to financial data is:  Statistics and Finance (Ruppert)



More information is available at https://sites.google.com/a/crlstatistics.net/finecon/



 
Useful R websites include:


Zurich University of Applied Sciences (ZHAW-IDP)  Institute of Data Analysis and Process Design


ETH Zurich Rmetrics


Implementing QuantLib (Luigi Ballabio ebook)

PerformanceAnalytics: Econometric tools for performance and risk analysis
http://cran.r-project.org/web/packages/PerformanceAnalytics




Revolution Webinar: Portfolio Design, Optimization, and Stability Analysis


http://www.revolutionanalytics.com/news-events/free-webinars/2011/portfolio-design-optimization-stability-analysis/index.php


Presented Wednesday, January 26, 2011
Presenter: Diethelm Würtz and Mahendra Mehta, Rmetrics Association
 

Download the webinar presentation

View the on-demand replay of the webinar



More of interest:

http://quanttrader.info/public/





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