Correlation Analysis Between Bovespa and NYSE

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

Bovespa  vs.  NYSE

 Performance (%) 
      Timeline 

Pair Volatility

Assuming 30 trading days horizon, Bovespa is expected to generate 4.39 times more return on investment than NYSE. However, Bovespa is 4.39 times more volatile than NYSE. It trades about 0.14 of its potential returns per unit of risk. NYSE is currently generating about 0.2 per unit of risk. If you would invest  7,563,377  in Bovespa on August 26, 2018 and sell it today you would earn a total of  381,052  from holding Bovespa or generate 5.04% return on investment over 30 days.

Pair Corralation between Bovespa and NYSE

0.73
Time Period1 Month [change]
DirectionPositive 
StrengthSignificant
Accuracy100.0%
ValuesDaily Returns

Diversification

Poor diversification

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

 Predicted Return Density 
      Returns 

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