Some select on-the-run inter market positive correlations for your consideration: Canadian Dollar vs. Nymex WTI Crude 1 Month 0.869 3 Months 0.690 6 Months 0.780 1 Year 0.944 2 Years 0.785 Comex Gold vs. Comex Silver 1 Month 0.518 3 Months 0.820 6 Months 0.888 1 Year 0.943 2 Years 0.905 FTSE 100 vs DJ EuroStoxx 30 1 Month 0.866 3 Months 0.735 6 Months 0.912 1 Year 0.800 2 Years 0.723 ES vs China A50 Index (SGX) 1 Month 0.786 3 Months 0.264 6 Months 0.429 1 Year 0.812 2 Years 0.452 NOTE: Daily Close-to-Close
How about giving the novices a definition of a what an inter-market correlation actually is, and then explain what one set of these correlation numbers means.
no kidding, us spot fx traders have been following that CAD vs CL for a long time, and it get's even more interesting when you introduce the other 5 majors against it everything is correlated, some more than others if you understood timing you could probably trade the correlation as a wave moving from one instrument to the other In otherwords, a good energy analyst could still be trading that wave. I know we felt it in usd.cad, who knows where it is now?
> NOTE: Daily Close-to-Close Which gives artificially low results for asynchronous markets. Best is a snapshot at a fixed time, say 10 AM EST.
why a timeshot based on time? Why not one based on price, or volatility or something else outside the box most think in? Just to confess, 99.99% of every trading decision (and most others) I make is based on time. But I have no proof time is any better than any other snapshot. It just works for me because I can slow things down or speed them up according to track conditions.
if you are trading a correlation, how do you measure it? Or when do you measure it? What's special about a day or a week or a month? It starts out, bad news for cl is bad news for cad, but then what? you just start filling in blanks "bad news for cad is bad news for blank" and on and on it goes, and if you were good you could ride that wave all the way back to WTI.
This is a blend of the results from two different statistical tests using daily settlement information, on-the-run, over the time periods of 1,3,6 months and 1 and 2 years. I use this as a SCREEN to find possible inter market combinations to observe and test at much higher frequency timeframes for cointegration and lead-lag behaviors. Lots of screening, just a handful of promising correlations result in something to observe and test further. But, you do it in order to uncover the occasional gem.
It should be possible to find further gems: recurring seasonal dependencies/correlations/strengths/weaknesses... Another idea would be seasonal correlation analysis for common goods/commodities among countries, for example summer in nothern countries means winter in downunder and nz, and vice-versa... Ie. could maybe give a possibility for arbitrage in cross-border trading...