Correlation Between Global X and Invesco KBW
Can any of the company-specific risk be diversified away by investing in both Global X 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 Global X and Invesco KBW into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Global X SuperDividend and Invesco KBW High, you can compare the effects of market volatilities on Global X 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 Global X with a short position of Invesco KBW. Check out your portfolio center. Please also check ongoing floating volatility patterns of Global X and Invesco KBW.
Diversification Opportunities for Global X and Invesco KBW
-0.07 | Correlation Coefficient |
Good diversification
The 3 months correlation between Global and Invesco is -0.07. Overlapping area represents the amount of risk that can be diversified away by holding Global X SuperDividend and Invesco KBW High in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Invesco KBW High and Global X 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 Global X SuperDividend 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 High has no effect on the direction of Global X i.e., Global X and Invesco KBW go up and down completely randomly.
Pair Corralation between Global X and Invesco KBW
Given the investment horizon of 90 days Global X SuperDividend is expected to under-perform the Invesco KBW. But the etf apears to be less risky and, when comparing its historical volatility, Global X SuperDividend is 1.17 times less risky than Invesco KBW. The etf trades about -0.17 of its potential returns per unit of risk. The Invesco KBW High is currently generating about 0.0 of returns per unit of risk over similar time horizon. If you would invest 1,477 in Invesco KBW High on September 27, 2024 and sell it today you would lose (8.00) from holding Invesco KBW High or give up 0.54% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Global X SuperDividend vs. Invesco KBW High
Performance |
Timeline |
Global X SuperDividend |
Invesco KBW High |
Global X and Invesco KBW Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Global X and Invesco KBW
The main advantage of trading using opposite Global X and Invesco KBW positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Global X 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.Global X vs. Global X SuperDividend | Global X vs. Invesco KBW Premium | Global X vs. Global X SuperDividend | Global X vs. Invesco KBW High |
Invesco KBW vs. Direxion Daily Regional | Invesco KBW vs. iShares MSCI Europe | Invesco KBW vs. Fidelity MSCI Financials | Invesco KBW vs. Direxion Daily Financial |
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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