Pair Correlation Between Shanghai and OMXVGI

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

Pair Volatility

Assuming 30 trading days horizon, Shanghai is expected to generate 5.0 times less return on investment than OMXVGI. In addition to that, Shanghai is 1.55 times more volatile than OMXVGI. It trades about 0.02 of its total potential returns per unit of risk. OMXVGI is currently generating about 0.12 per unit of volatility. If you would invest  65,596  in OMXVGI on October 20, 2017 and sell it today you would earn a total of  444  from holding OMXVGI or generate 0.68% return on investment over 30 days.

Correlation Coefficient

Pair Corralation between Shanghai and OMXVGI


Time Period1 Month [change]
StrengthVery Weak
ValuesDaily Returns


Modest diversification

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

Comparative Volatility

 Predicted Return Density