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 
If you run an e-commerce site, blog or other web property there's a good chance you use Google Analytics to monitor traffic, look at visitor sources, and measure conversions. And while Google Analytics is quite powerful at looking at historic activity on your site, it lacks much in the way of predictive analytics. That's where R shines of course,...
Drawing a scatterplot with the marginal boxplots (or marginal histograms or marginal density plots) has always been a bit tricky (well for me anyway). The approach I take here is, first, to draw the three separate plots using ggplot2:
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
