Most academic papers refer to relative volatility as one of two things:
1. The volatility of a security compared to that of an appropriate index.
or, closely related
2. The volatility of a security explained by an appropriate index.

Then there are technical factors such as the Relative Volatility Index, available for review here, although that isn’t really what I’m talking about.

I read Curtis Faith’s new book, and one thing that he mentions is something I’ve found interesting for a while: market regimes. He refers to them as market states and the info is available on pages 25 and 26 if you have the book. While this area of study has received some coverage, Faith breaks it down into a simple system that I like: Volatility and Trend (he uses the terms stable/trending and quiet/volatile). This creates four market regimes:

Low Volatility, Trending	High Volatility, Trending
Low Volatility, Not Trending	High Volatility, Not Trending

The volatility component that describes the regime is what I call (and he may have called too, don’t remember exactly) relative volatility. My interpretation differs from the academic, in that I would like to compare the volatility of a security to its volatility at some point in history.My first foray into this was simply to create two series, the volatility xx periods back and a moving average xx periods back of the previously defined volatility. Standard deviation measures could be added to the moving average, I suppose, to determine relatively high and relatively low volatility - but I haven’t gone that far yet.This initial measure has shown some interesting results in individual futures contracts when combined with basic time-dependent always-in order techniques. My continuous contract transformation may be suboptimal though (linear instead of logarithmic, switch period, maybe?), because the results can be quite interesting when on high volume time periods for given individual contracts, but when testing on long-term continuous contracts the results aren’t nearly as interesting/profitable. Still, this measure may provide a very slight edge if partnered with correct money management and order entry rules.

I’ll post more as this volatility measure develops. A quick Google, JSTOR, and Blackwell Synergy search didn’t pull up too much in this vein, rather the aforementioned definitions, so I plan to look more. But, if anyone has any ideas or has come across similar work, please comment and let me know!

No posts for a few days as I have been busy working on a couple of projects, plus I’ve started studying for the CFA L2 exam. One project is relative volatility, which I’ll get to in my next post; the second is an SMS trade confirmation system. This last one has really been getting to me.

So far, I’ve been able to get the system to work as long as it is attached to a trading strategy. The code is written in EasyLanguage and therefore amateurish at best, although I did review the methods for implementing it using C++. Still, I can’t get the program to run on its own without a strategy attached. However, when it works, it is very nice: I get a text message sent to my phone in the following structure:

where 1035 is the time, in this case 10:35a exchange time.

The first response coming to anyone’s mind, I’m sure, is:

Why would you want this? And please don’t say its because you leave your computer unattended while a system runs!

Yes, I do. I constantly monitor the system and have controls as best possible in place, but at times it is unattended. As a backup, I do maintain contact via remote access, which I can connect to if necessary. Also, my computer is set with back up resources (ie power).

I will put the completed code for the messaging system up as soon as I get all of the bugs worked out. It will be featured in the Models section.

And, while I certainly don’t encourage it, if anyone else leaves moderate- to high-frequency systems unattended, I’d be interested to hear your experiences - including the horror stories.

Update: I uploaded version 1.1 of the SMS system to the Models page. The code output is dependent upon the order type, but really this version is designed for a single contract system. I will add additional versions that accommodate multiple contract/share positions as well as partial/ladder sells/buys.

Inductive reasoning in trading may be the preferred method for developing systems. One reason, as argued by Doyne Farmer in Cracking Wall Street, is due to Local Predictability. The concept is based in chaos theory, and references the idea that there may be an underlying order in the short term that enables prediction of events that are not equally predictable, if at all, in the long term.

Farmer was a founder of Prediction Company, which was purchased by UBS. There is an interesting presentation Prediction Company created a few years back, titled The Business of Model Based Trading. Both the article and the attached .pdf are worth a quick read.

In the referenced article, there are a number of analogies and layman interpretations of the interaction between physics, math, and finance. Below are two passages that I enjoyed:

He likes to use a favorite example when explaining the anatomy of a prediction. “Here, catch this!” he says, tossing you a ball. You grab it. “You know how you caught that?” he asks. “By prediction.” Farmer contends you have a model in your head of how baseballs fly. You could predict the trajectory of a high fly using Newton’s classic equation f=ma, but your brain doesn’t stock up on elementary physics equations. Rather, it builds a model directly from experiential data. A baseball player watches a thousand baseballs come off a bat, and a thousand times lifts his gloved hand, and a thousand times adjusts his guess with his mitt. Without his knowing how, his brain gradually compiles a model of where the ball lands — a model almost as good as f=ma, but not as generalized. It’s based entirely on a collection of hand-eye data from past catches.

And:

While running from lions, or investing in stocks, the tiniest edge over raw luck is significant.