Pair Correlation Between DOW and OMX COPENHAGEN

This module allows you to analyze existing cross correlation between DOW and OMX COPENHAGEN. You can compare the effects of market volatilities on DOW and OMX COPENHAGEN and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in DOW with a short position of OMX COPENHAGEN. See also your portfolio center. Please also check ongoing floating volatility patterns of DOW and OMX COPENHAGEN.
 Time Horizon     30 Days    Login   to change
 Performance (%) 

Pair Volatility

Given the investment horizon of 30 days, DOW is expected to under-perform the OMX COPENHAGEN. In addition to that, DOW is 1.78 times more volatile than OMX COPENHAGEN. It trades about -0.12 of its total potential returns per unit of risk. OMX COPENHAGEN is currently generating about -0.17 per unit of volatility. If you would invest  138,500  in OMX COPENHAGEN on January 22, 2018 and sell it today you would lose (5,023)  from holding OMX COPENHAGEN or give up 3.63% of portfolio value over 30 days.

Correlation Coefficient

Pair Corralation between DOW and OMX COPENHAGEN


Time Period1 Month [change]
ValuesDaily Returns


Very poor diversification

Overlapping area represents the amount of risk that can be diversified away by holding DOW and OMX COPENHAGEN in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on OMX COPENHAGEN and DOW is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on DOW are associated (or correlated) with OMX COPENHAGEN. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of OMX COPENHAGEN has no effect on the direction of DOW i.e. DOW and OMX COPENHAGEN go up and down completely randomly.

Comparative Volatility

 Predicted Return Density