Pair Correlation Between DOW and iPath US

This module allows you to analyze existing cross correlation between DOW and iPath US Treasury Steepener ETN. You can compare the effects of market volatilities on DOW and iPath US 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 iPath US. See also your portfolio center. Please also check ongoing floating volatility patterns of DOW and iPath US.
 Time Horizon     30 Days    Login   to change
 DOW  vs   iPath US Treasury Steepener ET
 Performance (%) 

Pair Volatility

Given the investment horizon of 30 days, DOW is expected to generate 0.39 times more return on investment than iPath US. However, DOW is 2.54 times less risky than iPath US. It trades about 0.66 of its potential returns per unit of risk. iPath US Treasury Steepener ETN is currently generating about -0.14 per unit of risk. If you would invest  2,472,665  in DOW on December 20, 2017 and sell it today you would earn a total of  138,900  from holding DOW or generate 5.62% return on investment over 30 days.

Correlation Coefficient

Pair Corralation between DOW and iPath US


Time Period1 Month [change]
ValuesDaily Returns


Good diversification

Overlapping area represents the amount of risk that can be diversified away by holding DOW and iPath US Treasury Steepener ET in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on iPath US Treasury 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 iPath US. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of iPath US Treasury has no effect on the direction of DOW i.e. DOW and iPath US go up and down completely randomly.

Comparative Volatility

 Predicted Return Density