Pair Correlation Between Bovespa and NQFI

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

Pair Volatility

Assuming 30 trading days horizon, Bovespa is expected to generate 1.51 times more return on investment than NQFI. However, Bovespa is 1.51 times more volatile than NQFI. It trades about -0.09 of its potential returns per unit of risk. NQFI is currently generating about -0.22 per unit of risk. If you would invest  7,667,114  in Bovespa on October 25, 2017 and sell it today you would lose (215,235)  from holding Bovespa or give up 2.81% of portfolio value over 30 days.

Correlation Coefficient

Pair Corralation between Bovespa and NQFI
0.61

Parameters

Time Period1 Month [change]
DirectionPositive 
StrengthSignificant
Accuracy86.96%
ValuesDaily Returns

Diversification

Poor diversification

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

 Predicted Return Density 
      Returns