Pair Correlation Between NZSE and DOW

This module allows you to analyze existing cross correlation between NZSE and DOW. You can compare the effects of market volatilities on NZSE and DOW 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 NZSE with a short position of DOW. See also your portfolio center. Please also check ongoing floating volatility patterns of NZSE and DOW.
Investment Horizon     30 Days    Login   to change
Symbolsvs
 NZSE  vs   DOW
 Performance (%) 
      Timeline 

Pair Volatility

Assuming 30 trading days horizon, NZSE is expected to generate 4.45 times less return on investment than DOW. In addition to that, NZSE is 1.17 times more volatile than DOW. It trades about 0.02 of its total potential returns per unit of risk. DOW is currently generating about 0.11 per unit of volatility. If you would invest  2,332,946  in DOW on October 25, 2017 and sell it today you would earn a total of  19,672  from holding DOW or generate 0.84% return on investment over 30 days.

Correlation Coefficient

Pair Corralation between NZSE and DOW
0.02

Parameters

Time Period1 Month [change]
DirectionPositive 
StrengthInsignificant
Accuracy95.65%
ValuesDaily Returns

Diversification

Significant diversification

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

 Predicted Return Density 
      Returns