Pair Correlation Between Shanghai and IPC

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

Pair Volatility

Assuming 30 trading days horizon, Shanghai is expected to under-perform the IPC. In addition to that, Shanghai is 1.32 times more volatile than IPC. It trades about -0.61 of its total potential returns per unit of risk. IPC is currently generating about -0.19 per unit of volatility. If you would invest  5,026,025  in IPC on January 23, 2018 and sell it today you would lose (172,466)  from holding IPC or give up 3.43% of portfolio value over 30 days.

Correlation Coefficient

Pair Corralation between Shanghai and IPC
0.18

Parameters

Time Period1 Month [change]
DirectionPositive 
StrengthInsignificant
Accuracy71.43%
ValuesDaily Returns

Diversification

Average diversification

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

 Predicted Return Density 
      Returns