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Some advantages of R over Excel: R is a scripting language, which allows to record a data manipulation script once and reuse it multiple times. R, as a [scripting] programming language is much more flexible than very limited Excel's GUI. In fact, R has become a de facto statistical programming environment, which delivers most recent statistical techniques. ...


It is very hard to answer this quiz as people might be good at different at tools. For example, if you are good at VBA, then you can achieve the same effect compared to R in most cases. The following parts are the reasons why I prefer to R based on my own situation. 'package'. This is the most obvious strength of R over Excel in terms of convenience. You ...


This book by Shumway and Stoffer (two Pitt Stats profs) is excellent IMO: Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics): 9781441978646 http://www.amazon.com/Time-Series-Analysis-Its-Applications/dp/144197864X


It is all in the code:: Rcpp::List rl = Rcpp::List::create(Rcpp::Named("value") = opt.NPV(), Rcpp::Named("delta") = opt.delta(), Rcpp::Named("gamma") = opt.gamma(), Rcpp::Named("vega") = (excType=="european") ? opt.vega() : R_NaN, ...


This simply suggests the linear model is a poor fit in high frequency. But is this that surprising, even before you crunch the numbers? I argue not, for the following reasons: Even at low frequencies (i.e. monthly or annually), it is known that the classical CAPM (which is what you're running, albeit at a much higher frequency) does not fit well. It'd be ...


A high R-squared (1.0) means that you can explain the movements of one time series using the other. The lower your R-squared is, the worse your explanation is -- that includes the 'quality' of your beta. You can try to improve your R-squared score using different regression types. Beware of overfitting.

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