Pair Correlation Between DOW and SPDR SP

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

Pair Volatility

Given the investment horizon of 30 days, DOW is expected to under-perform the SPDR SP. In addition to that, DOW is 1.29 times more volatile than SPDR SP Dividend ETF. It trades about -0.04 of its total potential returns per unit of risk. SPDR SP Dividend ETF is currently generating about -0.01 per unit of volatility. If you would invest  9,228  in SPDR SP Dividend ETF on February 20, 2018 and sell it today you would lose (15.00)  from holding SPDR SP Dividend ETF or give up 0.16% of portfolio value over 30 days.

Correlation Coefficient

Pair Corralation between DOW and SPDR SP


Time Period1 Month [change]
ValuesDaily Returns


Average diversification

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

Comparative Volatility

 Predicted Return Density