Correlation Analysis Between DAX and Stockholm

This module allows you to analyze existing cross correlation between DAX and Stockholm. You can compare the effects of market volatilities on DAX and Stockholm 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 Stockholm. See also your portfolio center. Please also check ongoing floating volatility patterns of DAX and Stockholm.
Horizon     30 Days    Login   to change
Symbolsvs

DAX  vs.  Stockholm

 Performance (%) 
      Timeline 

Pair Volatility

Assuming 30 trading days horizon, DAX is expected to under-perform the Stockholm. In addition to that, DAX is 1.15 times more volatile than Stockholm. It trades about -0.07 of its total potential returns per unit of risk. Stockholm is currently generating about -0.01 per unit of volatility. If you would invest  60,123  in Stockholm on August 20, 2018 and sell it today you would lose (154.00)  from holding Stockholm or give up 0.26% of portfolio value over 30 days.

Pair Corralation between DAX and Stockholm

0.81
Time Period1 Month [change]
DirectionPositive 
StrengthStrong
Accuracy100.0%
ValuesDaily Returns

Diversification

Very poor diversification

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

 Predicted Return Density 
      Returns 

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