Correlation Between ProShares Ultra and Invesco KBW
Can any of the company-specific risk be diversified away by investing in both ProShares Ultra and Invesco KBW 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 ProShares Ultra and Invesco KBW into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between ProShares Ultra QQQ and Invesco KBW Bank, you can compare the effects of market volatilities on ProShares Ultra and Invesco KBW 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 ProShares Ultra with a short position of Invesco KBW. Check out your portfolio center. Please also check ongoing floating volatility patterns of ProShares Ultra and Invesco KBW.
Diversification Opportunities for ProShares Ultra and Invesco KBW
0.84 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between ProShares and Invesco is 0.84. Overlapping area represents the amount of risk that can be diversified away by holding ProShares Ultra QQQ and Invesco KBW Bank in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Invesco KBW Bank and ProShares Ultra 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 ProShares Ultra QQQ are associated (or correlated) with Invesco KBW. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Invesco KBW Bank has no effect on the direction of ProShares Ultra i.e., ProShares Ultra and Invesco KBW go up and down completely randomly.
Pair Corralation between ProShares Ultra and Invesco KBW
Considering the 90-day investment horizon ProShares Ultra QQQ is expected to under-perform the Invesco KBW. In addition to that, ProShares Ultra is 1.82 times more volatile than Invesco KBW Bank. It trades about -0.11 of its total potential returns per unit of risk. Invesco KBW Bank is currently generating about -0.04 per unit of volatility. If you would invest 6,492 in Invesco KBW Bank on December 30, 2024 and sell it today you would lose (297.00) from holding Invesco KBW Bank or give up 4.57% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
ProShares Ultra QQQ vs. Invesco KBW Bank
Performance |
Timeline |
ProShares Ultra QQQ |
Invesco KBW Bank |
ProShares Ultra and Invesco KBW Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with ProShares Ultra and Invesco KBW
The main advantage of trading using opposite ProShares Ultra and Invesco KBW positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if ProShares Ultra position performs unexpectedly, Invesco KBW 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 KBW will offset losses from the drop in Invesco KBW's long position.ProShares Ultra vs. ProShares Ultra SP500 | ProShares Ultra vs. ProShares UltraShort QQQ | ProShares Ultra vs. ProShares Ultra Dow30 | ProShares Ultra vs. ProShares Ultra Russell2000 |
Invesco KBW vs. Invesco KBW Regional | Invesco KBW vs. SPDR SP Bank | Invesco KBW vs. SPDR SP Regional | Invesco KBW vs. iShares Regional Banks |
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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