Ok, there is some meat there. Let me digest it...
nitro
nitro
Quote from Satyrican:
Fair enough.
Yes, Cointegration measure long term stabiilty but you forgetting that you can measure the speed of mean reversion! Which is the heart of stats arb.
In addition with vector autoregression (VAR), you can measure the speed of mean reversion for Any set of variable, while at the same time, "holding" other variable constant.
With regard to correlation, you can show that correlation converges into a stationary process if you increase the number of observation. Spurious correlation. Thus very little value to traders.
You can also show that in the SHORT RUN, the time frame of interest, volatility dominate return ~white noise if you will. Thus correlation based on RETURN on a short time frame offers very little info. You can test this by running a simple Monte Carlo (sp?) setup with two randomized sets. In the short run, volatility, not return, influence correlation calculation. Cointegration is "established" as a mean of correcting this all important spurious correlation.
Compounding the problem is the fact of the length of computation for correlation. How do you decide?
Your comment:"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."
Wrong. The "standard process" for stats arb is to run VAR and use the mean speed of reversion as your catalyst, not correlation dynamics. Why would you use correlation model? With VAR you can "hold" other parameters constants like interest rate fluctuation, implied volatility, etc. Correlation does not, it is very very crude. Unless your're talking about some nonlinear correlation - which I never heard of it - I dont see the sense of using correlation analysis.
Moreover, residual analysis would have been a better bet in of itself. If you already established with cointegration the cointegration vector, why would you need correlation???? You can simply use that cointegration vector and ECM, that would suffice. Where does correlation fall under?
----Satyrican
