Pair Correlation Between Shanghai and DOW

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

Pair Volatility

Assuming 30 trading days horizon, Shanghai is expected to generate 1.14 times more return on investment than DOW. However, Shanghai is 1.14 times more volatile than DOW. It trades about 0.02 of its potential returns per unit of risk. DOW is currently generating about 0.02 per unit of risk. If you would invest  337,865  in Shanghai on October 20, 2017 and sell it today you would earn a total of  426  from holding Shanghai or generate 0.13% return on investment over 30 days.

Correlation Coefficient

Pair Corralation between Shanghai and DOW
0.3

Parameters

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

Diversification

Weak diversification

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

 Predicted Return Density 
      Returns