Correlation Between UBS ETRACS and Innovator Buffer
Can any of the company-specific risk be diversified away by investing in both UBS ETRACS and Innovator Buffer 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 UBS ETRACS and Innovator Buffer into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between UBS ETRACS and Innovator Buffer Step Up, you can compare the effects of market volatilities on UBS ETRACS and Innovator Buffer 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 UBS ETRACS with a short position of Innovator Buffer. Check out your portfolio center. Please also check ongoing floating volatility patterns of UBS ETRACS and Innovator Buffer.
Diversification Opportunities for UBS ETRACS and Innovator Buffer
-0.56 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between UBS and Innovator is -0.56. Overlapping area represents the amount of risk that can be diversified away by holding UBS ETRACS and Innovator Buffer Step Up in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Innovator Buffer Step and UBS ETRACS 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 UBS ETRACS are associated (or correlated) with Innovator Buffer. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Innovator Buffer Step has no effect on the direction of UBS ETRACS i.e., UBS ETRACS and Innovator Buffer go up and down completely randomly.
Pair Corralation between UBS ETRACS and Innovator Buffer
Given the investment horizon of 90 days UBS ETRACS is expected to generate 10.64 times more return on investment than Innovator Buffer. However, UBS ETRACS is 10.64 times more volatile than Innovator Buffer Step Up. It trades about 0.15 of its potential returns per unit of risk. Innovator Buffer Step Up is currently generating about 0.18 per unit of risk. If you would invest 1,681 in UBS ETRACS on September 13, 2024 and sell it today you would earn a total of 169.90 from holding UBS ETRACS or generate 10.11% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
UBS ETRACS vs. Innovator Buffer Step Up
Performance |
| Timeline |
| UBS ETRACS |
| Innovator Buffer Step |
UBS ETRACS and Innovator Buffer Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with UBS ETRACS and Innovator Buffer
The main advantage of trading using opposite UBS ETRACS and Innovator Buffer positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if UBS ETRACS position performs unexpectedly, Innovator Buffer 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 Innovator Buffer will offset losses from the drop in Innovator Buffer's long position.| UBS ETRACS vs. Ultimus Managers Trust | UBS ETRACS vs. Direxion Daily SP | UBS ETRACS vs. EA Series Trust | UBS ETRACS vs. Global X MLP |
| Innovator Buffer vs. First Trust Cboe | Innovator Buffer vs. FT Cboe Vest | Innovator Buffer vs. Innovator SP 500 | Innovator Buffer vs. Innovator SP 500 |
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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