Pair Correlation Between FTSE MIB and NZSE

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

Pair Volatility

Assuming 30 trading days horizon, FTSE MIB is expected to under-perform the NZSE. In addition to that, FTSE MIB is 1.68 times more volatile than NZSE. It trades about -0.1 of its total potential returns per unit of risk. NZSE is currently generating about -0.07 per unit of volatility. If you would invest  813,010  in NZSE on October 23, 2017 and sell it today you would lose (4,961)  from holding NZSE or give up 0.61% of portfolio value over 30 days.

Correlation Coefficient

Pair Corralation between FTSE MIB and NZSE
-0.44

Parameters

Time Period1 Month [change]
DirectionNegative 
StrengthVery Weak
Accuracy90.48%
ValuesDaily Returns

Diversification

Very good diversification

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

Comparative Volatility

 Predicted Return Density 
      Returns