Correlation Analysis Between NQTH and NQFI

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


 Performance (%) 

Pair Volatility

Assuming 30 trading days horizon, NQTH is expected to generate 0.71 times more return on investment than NQFI. However, NQTH is 1.4 times less risky than NQFI. It trades about -0.07 of its potential returns per unit of risk. NQFI is currently generating about -0.06 per unit of risk. If you would invest  112,940  in NQTH on November 12, 2018 and sell it today you would lose (3,600)  from holding NQTH or give up 3.19% of portfolio value over 30 days.

Pair Corralation between NQTH and NQFI

Time Period2 Months [change]
ValuesDaily Returns


NQTH diversification synergy

Very weak diversification

Overlapping area represents the amount of risk that can be diversified away by holding NQTH and NQFI in the same portfolio assuming nothing else is changed. The correlation between historical prices or returns on NQFI 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 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 NQTH i.e. NQTH and NQFI go up and down completely randomly.

Comparative Volatility

 Predicted Return Density 

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See also your portfolio center. Please also try Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.