Hurst Exponent

This is the first post in a two-part series about the Hurst Exponent. Tom and I worked on this series together and I drew on some of his previously published work as well as other sources like the very useful Quantstart.com. UPDATE 03/01/16: Please note that the Python code below has been updated with a more accurate algorithm for calculating Hurst. Thanks Mike at Quantstart.com.   Mean-reverting time series have long been a fruitful playground for quantitative traders. In fact, some of the biggest names in quant trading allegedly made their fortunes exploiting mean reversion of financial time series such as artificially constructed spreads, which are used in pairs trading. Identifying mean reversion is therefore of significant interest to algorithmic traders. This is not as simple as it sounds, in part due to the non-stationary nature of financial data.   We both think that Ernie Chan's book “Algorithmic Trading: Winning Strategies and Their Rationale”, is one of the better introductions to mean reversion available in the public domain. In the book, Ernie talks about several tools that can be used when testing if a time series...

This series of posts is inspired by several chapters from Ernie Chan's highly recommended book Algorithmic Trading. The book follows Ernie's first contribution, Quantitative Trading, and focuses on testing and implementing a number of strategies that exploit measurable market inefficiencies. I'm a big fan of Ernie's work and have used his material as inspiration for a great deal of my own research. My earlier posts about accounting for randomness (here and here) were inspired by the first chapter of Algorithmic Trading. Ernie works in MATLAB, but I'll be using R and Zorro. Ernie cites Daniel Kahneman's interesting example of mean reversion in the world around us: the Sports Illustrated jinx, namely that "an athlete whose picture appears on the cover of the magazine is doomed to perform poorly the following season" (Kahneman, 2011). Performance can be thought of as being randomly distributed around a mean, so exceptionally good performance one year (resulting in the appearance on the cover of Sports Illustrated) is likely to be followed by performances that are closer to the average. Mean reversion also exists in, or can be constructed from, financial time series...