Recently I had several discussions about using for loops in GNU R and how they compare to *apply family in terms of speed. I have not seen a direct benchmark comparing them so I decided to execute one (warning: some of the code presented today tak...
Recently, for a research paper, I some samples, and I wanted to compare them. Not to compare they means (by construction, all of them were centered) but there dispersion. And not they variance, but more their quantiles. Consider the following boxplot type function, where everything here is quantile related (which is not the case for standard boxplot, see http://freakonometrics.hypotheses.org/4138,...
What effect do predicted correlations have when optimizing trades? Background A concern about optimization that is not one of “The top 7 portfolio optimization problems” is that correlations spike during a crisis which is when you most want optimization to work. This post looks at a small piece of that question. It wonders if increasing predicted … Continue reading...
In part 1, we took a look at VIX and the relationship it had between historical volatility and realized volatility.Continuing on, I thought I would take a look at next day returns and the VIX. There is a relationship between SPX and VIX in that when SP...
I’m a big fan of ggplot2. Recently, I ran into a situation which called for a useful feature that I had not used previously: aes_string. Imagine that you have data consisting of observations for several variables – let’s say A, B, C – where each observation is from one of two groups – call them 
Exploring the quality of predictions using random portfolios and optimization. Previously “Simple tests of predicted returns” showed a few ways to look at expected returns at the asset level. Here we move to the portfolio level. The previous post focused on correlation. Win Vector Blog points out that gauging prediction quality using correlation can be … Continue reading...
Some ways to explore how good a method of predicting returns is. Data and model The universe is 443 large cap US stocks that have data back to the beginning of 2004. The daily (adjusted) close was used. The model that is used as an example is the default signal from the MACD function of … Continue reading...
A prediction of a portfolio’s volatility is an estimate — how variable is that estimate? Data The universe is 453 large cap US stocks. The variance matrices are estimated with the daily returns in 2012. Variance estimation was done with Ledoit-Wolf shrinkage (shrinking towards equal correlation). Two sets of random portfolios were created. In both … Continue reading...
Following directly on from F1Stats – Visually Comparing Qualifying and Grid Positions with Race Classification, and continuing in my attempt to replicate some of the methodology and results used in A Tale of Two Motorsports: A Graphical-Statistical Analysis of How Practice, Qualifying, and Past SuccessRelate to Finish Position in NASCAR and Formula One Racing, here’s 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
