Here you will find daily news and tutorials about R, contributed by over 573 bloggers.
There are many ways to follow us - By e-mail:On Facebook: If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here)

In this post, Portfolio Probe explores a way to decide whether market kurtosis and skewness are predictable.

Market skewness, in naive financial modeling, is some kind of measure of (as-)symmetrical distribution of (daily) returns around the average market return. A higher skewness would tend to indicate a denser distribution of higher returns, compared to lower or negative returns.

In the cited example, skewness was estimated based on even partition of years since 2008. While is this is a neat idea, it seems like a good idea to study the evolution of a rolling skewness (skewness of returns of the preceding n days).

Below is a quick piece of R code to describe the distribution / fluctuation of a 30-day rolling skewness of the S&P 500 daily returns since 1980.

Surprisingly, the skewness is rather volatile, with sudden high negative values. The distribution of rolling skewness is negatively skewed as well.