Pair Correlation Between BSE and DOW

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

Pair Volatility

Assuming 30 trading days horizon, BSE is expected to under-perform the DOW. But the index apears to be less risky and, when comparing its historical volatility, BSE is 2.43 times less risky than DOW. The index trades about -0.47 of its potential returns per unit of risk. The DOW is currently generating about -0.14 of returns per unit of risk over similar time horizon. If you would invest  2,621,081  in DOW on January 23, 2018 and sell it today you would lose (141,303)  from holding DOW or give up 5.39% of portfolio value over 30 days.

Correlation Coefficient

Pair Corralation between BSE and DOW
0.81

Parameters

Time Period1 Month [change]
DirectionPositive 
StrengthStrong
Accuracy90.48%
ValuesDaily Returns

Diversification

Very poor diversification

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

Comparative Volatility

 Predicted Return Density 
      Returns