Diversity is a real strength. By now it is common knowledge. I often see institutions openly encourage multinational environment and multidisciplinary professionals, with specific “on-the-job” training to tailor for own needs. No one knows a lot about a lot, so bringing different together enhance independent thinking and knowledge available to the organization. Clarity of communication then becomes even more important, and making sure your figures are quickly understandable goes a long way.
One of my Ph.D papers was published recently. It deals with yield curve forecasting.
Here is the code for applying the Nelson-Siegel model to any yield curve.
Package Eplot on cran.
At least for me, R by faR. MATLAB has its own way of doing things, which to be honest can probably be defended from many angles. Here are few examples for not so subtle differences between R and MATLAB:
R takes it. I prefer coding in R over MATLAB. I feel R understands that I do not like to type too much. A few examples:
In the last few decades there has been tremendous progress in the realm of volatility estimation. A major step is the additional use of intraday price path. It has been shown that estimates which consider intraday information are more accurate. Which is to say they converge faster to the real unobserved value of the true volatility.
The summary function in R returns:
Min. 1st Qu. Median Mean 3rd Qu. Max.
9.14 10.70 11.10 11.30 12.10 13.60
For the univariate case I wrote what I consider to be a better summary function which returns:
usum(x) # For univariate Summary
min med mean max sd skew kurt
1 9.14 11.13 11.35 13.65 1.057 0.3028 -0.6389
No NA's in the series
13.65 13.55 13.08 13.13