Pair Correlation Between IPC and Shanghai

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

Pair Volatility

Given the investment horizon of 30 days, IPC is expected to under-perform the Shanghai. In addition to that, IPC is 1.97 times more volatile than Shanghai. It trades about -0.26 of its total potential returns per unit of risk. Shanghai is currently generating about 0.02 per unit of volatility. 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 IPC and Shanghai
-0.46

Parameters

Time Period1 Month [change]
DirectionNegative 
StrengthVery Weak
Accuracy95.45%
ValuesDaily Returns

Diversification

Very good diversification

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

 Predicted Return Density 
      Returns