Correlation Between United States and Invesco DB
Can any of the company-specific risk be diversified away by investing in both United States and Invesco DB 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 United States and Invesco DB into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between United States 12 and Invesco DB Energy, you can compare the effects of market volatilities on United States and Invesco DB 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 United States with a short position of Invesco DB. Check out your portfolio center. Please also check ongoing floating volatility patterns of United States and Invesco DB.
Diversification Opportunities for United States and Invesco DB
0.99 | Correlation Coefficient |
No risk reduction
The 3 months correlation between United and Invesco is 0.99. Overlapping area represents the amount of risk that can be diversified away by holding United States 12 and Invesco DB Energy in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Invesco DB Energy and United States 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 United States 12 are associated (or correlated) with Invesco DB. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Invesco DB Energy has no effect on the direction of United States i.e., United States and Invesco DB go up and down completely randomly.
Pair Corralation between United States and Invesco DB
Considering the 90-day investment horizon United States is expected to generate 1.16 times less return on investment than Invesco DB. In addition to that, United States is 1.05 times more volatile than Invesco DB Energy. It trades about 0.03 of its total potential returns per unit of risk. Invesco DB Energy is currently generating about 0.04 per unit of volatility. If you would invest 1,827 in Invesco DB Energy on September 13, 2024 and sell it today you would earn a total of 59.00 from holding Invesco DB Energy or generate 3.23% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
United States 12 vs. Invesco DB Energy
Performance |
Timeline |
United States 12 |
Invesco DB Energy |
United States and Invesco DB Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with United States and Invesco DB
The main advantage of trading using opposite United States and Invesco DB positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if United States position performs unexpectedly, Invesco DB 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 Invesco DB will offset losses from the drop in Invesco DB's long position.United States vs. Invesco DB Oil | United States vs. United States Gasoline | United States vs. United States Brent | United States vs. United States 12 |
Invesco DB vs. Invesco DB Precious | Invesco DB vs. Invesco DB Base | Invesco DB vs. Invesco DB Oil | Invesco DB vs. Invesco DB Agriculture |
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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