Pair Correlation Between OMXRGI and NQTH

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

Pair Volatility

Assuming 30 trading days horizon, OMXRGI is expected to under-perform the NQTH. In addition to that, OMXRGI is 1.32 times more volatile than NQTH. It trades about -0.06 of its total potential returns per unit of risk. NQTH is currently generating about -0.02 per unit of volatility. 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 OMXRGI and NQTH
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 OMXRGI and NQTH in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on NQTH and OMXRGI 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 OMXRGI are associated (or correlated) with NQTH. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of NQTH has no effect on the direction of OMXRGI i.e. OMXRGI and NQTH go up and down completely randomly.
    Optimize

Comparative Volatility

 Predicted Return Density 
      Returns