Correlation Between Emerging Markets and Cm Modity
Can any of the company-specific risk be diversified away by investing in both Emerging Markets and Cm Modity at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining Emerging Markets and Cm Modity into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Emerging Markets Fund and Cm Modity Index, you can compare the effects of market volatilities on Emerging Markets and Cm Modity 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 Emerging Markets with a short position of Cm Modity. Check out your portfolio center. Please also check ongoing floating volatility patterns of Emerging Markets and Cm Modity.
Diversification Opportunities for Emerging Markets and Cm Modity
0.72 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Emerging and COMIX is 0.72. Overlapping area represents the amount of risk that can be diversified away by holding Emerging Markets Fund and Cm Modity Index in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Cm Modity Index and Emerging Markets 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 Emerging Markets Fund are associated (or correlated) with Cm Modity. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Cm Modity Index has no effect on the direction of Emerging Markets i.e., Emerging Markets and Cm Modity go up and down completely randomly.
Pair Corralation between Emerging Markets and Cm Modity
Assuming the 90 days horizon Emerging Markets Fund is expected to generate 1.13 times more return on investment than Cm Modity. However, Emerging Markets is 1.13 times more volatile than Cm Modity Index. It trades about 0.25 of its potential returns per unit of risk. Cm Modity Index is currently generating about 0.07 per unit of risk. If you would invest 1,468 in Emerging Markets Fund on April 27, 2025 and sell it today you would earn a total of 188.00 from holding Emerging Markets Fund or generate 12.81% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Emerging Markets Fund vs. Cm Modity Index
Performance |
Timeline |
Emerging Markets |
Cm Modity Index |
Emerging Markets and Cm Modity Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Emerging Markets and Cm Modity
The main advantage of trading using opposite Emerging Markets and Cm Modity positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Emerging Markets position performs unexpectedly, Cm Modity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Cm Modity will offset losses from the drop in Cm Modity's long position.Emerging Markets vs. Elfun Government Money | Emerging Markets vs. Voya Government Money | Emerging Markets vs. Payden Government Fund | Emerging Markets vs. Aim Investment Securities |
Cm Modity vs. Greenspring Fund Retail | Cm Modity vs. T Rowe Price | Cm Modity vs. Monteagle Enhanced Equity | Cm Modity vs. Smallcap World Fund |
Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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