Pair Correlation Between NQPH and Bovespa

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

Pair Volatility

Assuming 30 trading days horizon, NQPH is expected to generate 0.61 times more return on investment than Bovespa. However, NQPH is 1.63 times less risky than Bovespa. It trades about -0.03 of its potential returns per unit of risk. Bovespa is currently generating about -0.13 per unit of risk. If you would invest  118,385  in NQPH on October 20, 2017 and sell it today you would lose (747)  from holding NQPH or give up 0.63% of portfolio value over 30 days.

Correlation Coefficient

Pair Corralation between NQPH and Bovespa
-0.3

Parameters

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

Diversification

Very good diversification

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

Comparative Volatility

 Predicted Return Density 
      Returns