Correlation Between QBE Insurance and GALP ENERGIA
Can any of the company-specific risk be diversified away by investing in both QBE Insurance and GALP ENERGIA 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 QBE Insurance and GALP ENERGIA into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between QBE Insurance Group and GALP ENERGIA B , you can compare the effects of market volatilities on QBE Insurance and GALP ENERGIA 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 QBE Insurance with a short position of GALP ENERGIA. Check out your portfolio center. Please also check ongoing floating volatility patterns of QBE Insurance and GALP ENERGIA.
Diversification Opportunities for QBE Insurance and GALP ENERGIA
-0.71 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between QBE and GALP is -0.71. Overlapping area represents the amount of risk that can be diversified away by holding QBE Insurance Group and GALP ENERGIA B in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on GALP ENERGIA B and QBE Insurance 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 QBE Insurance Group are associated (or correlated) with GALP ENERGIA. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of GALP ENERGIA B has no effect on the direction of QBE Insurance i.e., QBE Insurance and GALP ENERGIA go up and down completely randomly.
Pair Corralation between QBE Insurance and GALP ENERGIA
Assuming the 90 days horizon QBE Insurance Group is expected to generate 0.96 times more return on investment than GALP ENERGIA. However, QBE Insurance Group is 1.05 times less risky than GALP ENERGIA. It trades about 0.14 of its potential returns per unit of risk. GALP ENERGIA B is currently generating about -0.04 per unit of risk. If you would invest 1,020 in QBE Insurance Group on October 1, 2024 and sell it today you would earn a total of 130.00 from holding QBE Insurance Group or generate 12.75% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
QBE Insurance Group vs. GALP ENERGIA B
Performance |
Timeline |
QBE Insurance Group |
GALP ENERGIA B |
QBE Insurance and GALP ENERGIA Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with QBE Insurance and GALP ENERGIA
The main advantage of trading using opposite QBE Insurance and GALP ENERGIA positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if QBE Insurance position performs unexpectedly, GALP ENERGIA 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 GALP ENERGIA will offset losses from the drop in GALP ENERGIA's long position.QBE Insurance vs. NURAN WIRELESS INC | QBE Insurance vs. Federal Agricultural Mortgage | QBE Insurance vs. Australian Agricultural | QBE Insurance vs. Tokyu Construction Co |
GALP ENERGIA vs. PKSHA TECHNOLOGY INC | GALP ENERGIA vs. Kingdee International Software | GALP ENERGIA vs. Apollo Medical Holdings | GALP ENERGIA vs. ONWARD MEDICAL BV |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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