Pair Correlation Between NQPH and All Ords

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

Pair Volatility

Assuming 30 trading days horizon, NQPH is expected to under-perform the All Ords. In addition to that, NQPH is 1.08 times more volatile than All Ords. It trades about -0.42 of its total potential returns per unit of risk. All Ords is currently generating about -0.04 per unit of volatility. If you would invest  616,470  in All Ords on January 25, 2018 and sell it today you would lose (5,950)  from holding All Ords or give up 0.97% of portfolio value over 30 days.

Correlation Coefficient

Pair Corralation between NQPH and All Ords
0.63

Parameters

Time Period1 Month [change]
DirectionPositive 
StrengthSignificant
Accuracy90.91%
ValuesDaily Returns

Diversification

Poor diversification

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

Comparative Volatility

 Predicted Return Density 
      Returns