...Your Comment: "I routinely crush the returns these guys yearly returns in two months of trading."
Please elaborate. Are you taking about return on a trader positon or an institution posiiton? Big Big difference. Moreover, how have the method fare so far?
I am trader, so returns are from a traders perspective. I no longer pair trade because I went cold on the method about a year ago, so I cannot tell you how the "method" is fairing so far. I have new incentives and ideas to make it work and I am working on a programming project to implement that tool.
...Your Comment: "Cointegration without correlation analysis is like a coin with one side."
Pure bullshit or sheer ignorance. If you need further discussion on this topic, visit Wilmott forum. This topic is discussed at tremendous LENGTH. Sorry if I seem harsh, but they way you shape your comment are very offensive.
Preamble:
a) Returns are short memory process. Correlation measures RETURNS. The traditional starting point for asset allocation and risk management is to DIFFERENCE (turn the raw data into returns) the price data before analysis is even begun (in the time domain), and differencing removes a priory any long term trends in the data.
b) Cointegration is based on the RAW price data, rate or yield data, AS WELL as the return data. Price, rate and yield data are not normally stationary - in fact they are usually integrated of order 1, I(1).
Definition: A set of I(1) series is termed 'cointegrated' if there is a linear combination of that series that is stationary. So in the case of two integrated series:
x and
y are cointegrated if
x,y ~ I(1) but there exists an
a such that
x - ay ~ I(0)
Algorithm: Cointegration is a TWO step PROCESS: first any long-run equilibrium realtionships between prices are established using Ordinary Least Squares (OLS) regression (plus some statistics to verify), and THEN a dynamic CORRELATION model of returns is estimated. The Error Correcting Model (ECM), so called because short-term deviations from equilibrium are corrected.
[I just explained cointegration to a bright high school kid - Notice that I am CORRECT in my assertion that correlation and cointegration go HAND IN HAND in some implementations of it]
Your Comment: "Anyone that does Pairs trading without doing _at_least_ correlation analysis is an amateur at best"
I know a good number of people who do not run correlation analysis. I guess they're not profession but rather amateur by your definition.
I guarantee you, they are running correlation analysis if they are SHORT TERM TRADERS and not "Investors" looking for a 10% yearly return.
Your Comment: "How long have you been on ET? This statement is just plain false. I have no idea whose pairs trading threads you have been looking at, but there are some seriously large pair traders at ET."
You're correct in the sense that I havent been here long, but from what I gleaned, that is my observation. I think it was NYSE's trend ... that really really long one. If I remember correctly all the strategies centered around TA. RSI, MA, Stochastic, etc. Moreover, Pairs trading and statistical arbitrage are DIFFERENT entity although they share similar characteristics.
Long/Short, or market neutral strategies are in "spirit" all the same and share most of the math with each other.
Your Comment: "I could explain correlation to a high school kid. I could explain cointegration to anyone with a decent grasp of calculus. This is SIMPLE and does not require a Phd in anything. "
Perhaps you're one of those great teachers or those with the articulation second to none. But explaining to correlation to a high school kid is a daunting feat, how do I know? I teach a night undergraduate class after I got my degree, if undergraduate are having difficult I can only imagine that it would be hard for HS students. You make it seems so easy and effortless. The basic concept of Cointegration is indeed easy, but how many undergraduate do you know that understand this??? Again, you're making it out to seem as if it is a cakewalk which I strongly disagree. VAR (not value at risk) is usually reserve for higher end master program and Phd. You cant just learn this via simple calculus.
The subject of Financial Engineering is not trivial by any means. However, see above for "my" explanation of cointegration - any bright high school kid with an inclination for math would understand that statement with no problem.
Your Comment: "All of Econometric and Financial Engineering is constantly evolving. Genetic Algorithms have been around ever since I started programming and have been (tried) used in the Financial marktets ever since I can remember. Fuzzy logic the same, and equally worthless (in the face of other statistical measures.)"
Perhaps you misunderstood or my syntax is bad... Genetic algorithms on pairs and fuzzy logic on PAIRS trading. "Black Box" has been here since the 70s if I remember correct.
ok.
Your comment: "No I have neither, but I stayed a Holiday Inn last night?"
Inside joke? I have no clue.
You must not be from the US. Yes it was a joke...
Your comment: "There is no replacing being a trader in any of this stuff. Most of these advanced math "traders" usually have to be carried out of a trading room on an oxygen tank..."
Agree. In fact, a number of firms I know hire traders for this very reason.
But here is the irony: cointegration is really meant to measure LONG TERM relations between variables, usually the time frame is not in the dominion of the trader.
Your Comment: "The hard part of most of Time Series Analysis is knowing when and how and why to apply what to the data. Getting the data "prepped" for TSA is harder than the application of the methods themselves. In fact, since these are all LINEAR MODELS, their usefulness is in question. But we do the best we can, and as traders, adjust accordingly..."
Agree except for the remarks on linearity. Nonlinear relationship can be model via linear methodsl time series is not exclusive to nonlinear relationship. There is a huge literature on nonlinear time series.... where the focus is turning nonlinearity into linear composition.
Well, if you have solved this problem, I would like to see it and probably would win you a Noble Prize in Economics. At the core of all these analysis and statistical measures is LINEAR ALGEBRA. That was the reason for trying to bring in Neural Nets, etc, to capture the nonlinearities in the data better...
Your Comment: "Model selection is not "chosen" by hand. The prefered way these days is to let MLE or other optimization functions tell you what the "best" model is. All these models are useful in some particular case..."
Agreed. But you're taking the comment out of context. My comment was for illustration purpose and NOT to argue on the merits of selection.
Ok.
P.S. I am curious, but what is your major? And how did you learn all this stuff which is equivalent to obtaining a MSFE?
I started an undergraduate degree in Algebraic Number Theory. I am well versed (but VERY rusty) in Groups, Rings, Modules, Vector Spaces, and Linear Algebra. Once you have even this relatively weak background, people can try to complicate the subject all they want, but it is pretty straight forward.
One thing that I find interesting is that you keep asking me questions, and you wave your hands alot by claiming all kinds of things, but not ONCE have you shown that YOU know what you are talking about. That is where my "venting/venom" comes from - you posture alot, but offer little...
nitro
I didnt notice how long my respond was.
