Pair Correlation Between Bovespa and ISEQ

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

Pair Volatility

Assuming 30 trading days horizon, Bovespa is expected to under-perform the ISEQ. In addition to that, Bovespa is 1.61 times more volatile than ISEQ. It trades about -0.12 of its total potential returns per unit of risk. ISEQ is currently generating about 0.12 per unit of volatility. If you would invest  674,746  in ISEQ on October 19, 2017 and sell it today you would earn a total of  14,881  from holding ISEQ or generate 2.21% return on investment over 30 days.

Correlation Coefficient

Pair Corralation between Bovespa and ISEQ
0.01

Parameters

Time Period1 Month [change]
DirectionPositive 
StrengthInsignificant
Accuracy95.45%
ValuesDaily Returns

Diversification

Significant diversification

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

Comparative Volatility

 Predicted Return Density 
      Returns