Pair Correlation Between NQTH and Russia TR

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

Pair Volatility

Assuming 30 trading days horizon, NQTH is expected to generate 0.45 times more return on investment than Russia TR. However, NQTH is 2.21 times less risky than Russia TR. It trades about -0.02 of its potential returns per unit of risk. Russia TR is currently generating about -0.06 per unit of risk. If you would invest  124,933  in NQTH on January 22, 2018 and sell it today you would lose (512.00)  from holding NQTH or give up 0.41% of portfolio value over 30 days.

Correlation Coefficient

Pair Corralation between NQTH and Russia TR
0.74

Parameters

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

Diversification

Poor diversification

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

Comparative Volatility

 Predicted Return Density 
      Returns