Pair Correlation Between Stockholm and NZSE

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

Pair Volatility

Assuming 30 trading days horizon, Stockholm is expected to under-perform the NZSE. In addition to that, Stockholm is 1.41 times more volatile than NZSE. It trades about -0.14 of its total potential returns per unit of risk. NZSE is currently generating about -0.03 per unit of volatility. If you would invest  813,010  in NZSE on October 24, 2017 and sell it today you would lose (2,114)  from holding NZSE or give up 0.26% of portfolio value over 30 days.

Correlation Coefficient

Pair Corralation between Stockholm and NZSE
0.22

Parameters

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

Diversification

Modest diversification

Overlapping area represents the amount of risk that can be diversified away by holding Stockholm and NZSE in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on NZSE and Stockholm 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 Stockholm 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 Stockholm i.e. Stockholm and NZSE go up and down completely randomly.
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Comparative Volatility

 Predicted Return Density 
      Returns