Pair Correlation Between IBEX 35 and NYSE

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

Pair Volatility

Assuming 30 trading days horizon, IBEX 35 is expected to under-perform the NYSE. In addition to that, IBEX 35 is 3.0 times more volatile than NYSE. It trades about -0.08 of its total potential returns per unit of risk. NYSE is currently generating about 0.0 per unit of volatility. If you would invest  1,238,442  in NYSE on October 23, 2017 and sell it today you would earn a total of  147  from holding NYSE or generate 0.01% return on investment over 30 days.

Correlation Coefficient

Pair Corralation between IBEX 35 and NYSE
0.44

Parameters

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

Diversification

Very weak diversification

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

Comparative Volatility

 Predicted Return Density 
      Returns