Pair Correlation Between Shanghai and XU100

This module allows you to analyze existing cross correlation between Shanghai and XU100. You can compare the effects of market volatilities on Shanghai and XU100 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 XU100. See also your portfolio center. Please also check ongoing floating volatility patterns of Shanghai and XU100.
 Time Horizon     30 Days    Login   to change
Symbolsvs
 Shanghai  vs   XU100
 Performance (%) 
      Timeline 

Pair Volatility

Assuming 30 trading days horizon, Shanghai is expected to under-perform the XU100. In addition to that, Shanghai is 1.26 times more volatile than XU100. It trades about -0.47 of its total potential returns per unit of risk. XU100 is currently generating about -0.03 per unit of volatility. If you would invest  11,723,547  in XU100 on January 20, 2018 and sell it today you would lose (72,450)  from holding XU100 or give up 0.62% of portfolio value over 30 days.

Correlation Coefficient

Pair Corralation between Shanghai and XU100
0.6

Parameters

Time Period1 Month [change]
DirectionPositive 
StrengthSignificant
Accuracy94.12%
ValuesDaily Returns

Diversification

Poor diversification

Overlapping area represents the amount of risk that can be diversified away by holding Shanghai and XU100 in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on XU100 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 XU100. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of XU100 has no effect on the direction of Shanghai i.e. Shanghai and XU100 go up and down completely randomly.
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Comparative Volatility

 Predicted Return Density 
      Returns