Pair Correlation Between ISEQ and Shanghai

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

Pair Volatility

Assuming 30 trading days horizon, ISEQ is expected to generate 2.09 times more return on investment than Shanghai. However, ISEQ is 2.09 times more volatile than Shanghai. It trades about 0.1 of its potential returns per unit of risk. Shanghai is currently generating about 0.04 per unit of risk. If you would invest  682,468  in ISEQ on October 22, 2017 and sell it today you would earn a total of  11,657  from holding ISEQ or generate 1.71% return on investment over 30 days.

Correlation Coefficient

Pair Corralation between ISEQ and Shanghai
-0.23

Parameters

Time Period1 Month [change]
DirectionNegative 
StrengthInsignificant
Accuracy100.0%
ValuesDaily Returns

Diversification

Very good diversification

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

Comparative Volatility

 Predicted Return Density 
      Returns