Pair Correlation Between ISEQ and IBEX 35

This module allows you to analyze existing cross correlation between ISEQ and IBEX 35. You can compare the effects of market volatilities on ISEQ and IBEX 35 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 ISEQ with a short position of IBEX 35. See also your portfolio center. Please also check ongoing floating volatility patterns of ISEQ and IBEX 35.
Investment Horizon     30 Days    Login   to change
Symbolsvs
 ISEQ  vs   IBEX 35
 Performance (%) 
      Timeline 

Pair Volatility

Assuming 30 trading days horizon, ISEQ is expected to generate 0.93 times more return on investment than IBEX 35. However, ISEQ is 1.08 times less risky than IBEX 35. It trades about 0.05 of its potential returns per unit of risk. IBEX 35 is currently generating about -0.08 per unit of risk. If you would invest  677,358  in ISEQ on October 18, 2017 and sell it today you would earn a total of  6,595  from holding ISEQ or generate 0.97% return on investment over 30 days.

Correlation Coefficient

Pair Corralation between ISEQ and IBEX 35
0.7

Parameters

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

Diversification

Poor diversification

Overlapping area represents the amount of risk that can be diversified away by holding ISEQ and IBEX 35 in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on IBEX 35 and ISEQ 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 ISEQ are associated (or correlated) with IBEX 35. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of IBEX 35 has no effect on the direction of ISEQ i.e. ISEQ and IBEX 35 go up and down completely randomly.
    Optimize

Comparative Volatility

 Predicted Return Density 
      Returns