Pair Correlation Between OMXVGI and NZSE

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

Pair Volatility

Assuming 30 trading days horizon, OMXVGI is expected to generate 0.66 times more return on investment than NZSE. However, OMXVGI is 1.52 times less risky than NZSE. It trades about 0.1 of its potential returns per unit of risk. NZSE is currently generating about -0.09 per unit of risk. If you would invest  65,680  in OMXVGI on October 19, 2017 and sell it today you would earn a total of  360  from holding OMXVGI or generate 0.55% return on investment over 30 days.

Correlation Coefficient

Pair Corralation between OMXVGI and NZSE
-0.46

Parameters

Time Period1 Month [change]
DirectionNegative 
StrengthVery Weak
Accuracy100.0%
ValuesDaily Returns

Diversification

Very good diversification

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

Comparative Volatility

 Predicted Return Density 
      Returns