Correlation Between QBE Insurance and Transurban
Can any of the company-specific risk be diversified away by investing in both QBE Insurance and Transurban 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 Transurban 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 Transurban Group, you can compare the effects of market volatilities on QBE Insurance and Transurban 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 Transurban. Check out your portfolio center. Please also check ongoing floating volatility patterns of QBE Insurance and Transurban.
Diversification Opportunities for QBE Insurance and Transurban
0.05 | Correlation Coefficient |
Significant diversification
The 3 months correlation between QBE and Transurban is 0.05. Overlapping area represents the amount of risk that can be diversified away by holding QBE Insurance Group and Transurban Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Transurban Group 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 Transurban. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Transurban Group has no effect on the direction of QBE Insurance i.e., QBE Insurance and Transurban go up and down completely randomly.
Pair Corralation between QBE Insurance and Transurban
Assuming the 90 days horizon QBE Insurance Group is expected to generate 1.29 times more return on investment than Transurban. However, QBE Insurance is 1.29 times more volatile than Transurban Group. It trades about 0.19 of its potential returns per unit of risk. Transurban Group is currently generating about -0.05 per unit of risk. If you would invest 1,160 in QBE Insurance Group on December 2, 2024 and sell it today you would earn a total of 120.00 from holding QBE Insurance Group or generate 10.34% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
QBE Insurance Group vs. Transurban Group
Performance |
Timeline |
QBE Insurance Group |
Transurban Group |
QBE Insurance and Transurban Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with QBE Insurance and Transurban
The main advantage of trading using opposite QBE Insurance and Transurban positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if QBE Insurance position performs unexpectedly, Transurban 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 Transurban will offset losses from the drop in Transurban's long position.QBE Insurance vs. Molson Coors Beverage | QBE Insurance vs. LINMON MEDIA LTD | QBE Insurance vs. MOLSON RS BEVERAGE | QBE Insurance vs. Fevertree Drinks PLC |
Transurban vs. Suntory Beverage Food | Transurban vs. Ming Le Sports | Transurban vs. SQUIRREL MEDIA SA | Transurban vs. Fevertree Drinks PLC |
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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