Correlation Analysis Between OMXVGI and BSE

This module allows you to analyze existing cross correlation between OMXVGI and BSE. You can compare the effects of market volatilities on OMXVGI and BSE 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 OMXVGI with a short position of BSE. See also your portfolio center. Please also check ongoing floating volatility patterns of OMXVGI and BSE.
Horizon     30 Days    Login   to change


 Performance (%) 

Pair Volatility

Assuming 30 trading days horizon, OMXVGI is expected to generate 0.53 times more return on investment than BSE. However, OMXVGI is 1.9 times less risky than BSE. It trades about -0.14 of its potential returns per unit of risk. BSE is currently generating about -0.28 per unit of risk. If you would invest  68,849  in OMXVGI on September 19, 2018 and sell it today you would lose (1,500)  from holding OMXVGI or give up 2.18% of portfolio value over 30 days.

Pair Corralation between OMXVGI and BSE

Time Period1 Month [change]
StrengthVery Strong
ValuesDaily Returns


Almost no diversification

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

Comparative Volatility

 Predicted Return Density 

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See also your portfolio center. Please also try Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.