Pair Correlation Between Taiwan Wtd and BSE

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

Pair Volatility

Assuming 30 trading days horizon, Taiwan Wtd is expected to under-perform the BSE. In addition to that, Taiwan Wtd is 1.75 times more volatile than BSE. It trades about -0.26 of its total potential returns per unit of risk. BSE is currently generating about -0.28 per unit of volatility. If you would invest  3,551,158  in BSE on January 19, 2018 and sell it today you would lose (150,082)  from holding BSE or give up 4.23% of portfolio value over 30 days.

Correlation Coefficient

Pair Corralation between Taiwan Wtd and BSE
0.75

Parameters

Time Period1 Month [change]
DirectionPositive 
StrengthSignificant
Accuracy94.74%
ValuesDaily Returns

Diversification

Poor diversification

Overlapping area represents the amount of risk that can be diversified away by holding Taiwan Wtd and BSE in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on BSE and Taiwan Wtd 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 Taiwan Wtd 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 Taiwan Wtd i.e. Taiwan Wtd and BSE go up and down completely randomly.
    Optimize

Comparative Volatility

 Predicted Return Density 
      Returns