Pair Correlation Between DAX and Madrid Gnrl

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

Pair Volatility

Assuming 30 trading days horizon, DAX is expected to generate 1.09 times less return on investment than Madrid Gnrl. In addition to that, DAX is 1.13 times more volatile than Madrid Gnrl. It trades about 0.22 of its total potential returns per unit of risk. Madrid Gnrl is currently generating about 0.27 per unit of volatility. If you would invest  102,898  in Madrid Gnrl on December 22, 2017 and sell it today you would earn a total of  3,115  from holding Madrid Gnrl or generate 3.03% return on investment over 30 days.

Correlation Coefficient

Pair Corralation between DAX and Madrid Gnrl


Time Period1 Month [change]
ValuesDaily Returns


Very poor diversification

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

Comparative Volatility

 Predicted Return Density