Correlation Between QBE Insurance and Insurance Australia
Can any of the company-specific risk be diversified away by investing in both QBE Insurance and Insurance Australia 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 Insurance Australia 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 Insurance Australia Group, you can compare the effects of market volatilities on QBE Insurance and Insurance Australia 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 Insurance Australia. Check out your portfolio center. Please also check ongoing floating volatility patterns of QBE Insurance and Insurance Australia.
Diversification Opportunities for QBE Insurance and Insurance Australia
0.77 | Correlation Coefficient |
Poor diversification
The 3 months correlation between QBE and Insurance is 0.77. Overlapping area represents the amount of risk that can be diversified away by holding QBE Insurance Group and Insurance Australia Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Insurance Australia 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 Insurance Australia. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Insurance Australia has no effect on the direction of QBE Insurance i.e., QBE Insurance and Insurance Australia go up and down completely randomly.
Pair Corralation between QBE Insurance and Insurance Australia
Assuming the 90 days horizon QBE Insurance Group is expected to generate 0.69 times more return on investment than Insurance Australia. However, QBE Insurance Group is 1.45 times less risky than Insurance Australia. It trades about -0.07 of its potential returns per unit of risk. Insurance Australia Group is currently generating about -0.16 per unit of risk. If you would invest 1,290 in QBE Insurance Group on April 16, 2025 and sell it today you would lose (20.00) from holding QBE Insurance Group or give up 1.55% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 95.45% |
Values | Daily Returns |
QBE Insurance Group vs. Insurance Australia Group
Performance |
Timeline |
QBE Insurance Group |
Insurance Australia |
QBE Insurance and Insurance Australia Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with QBE Insurance and Insurance Australia
The main advantage of trading using opposite QBE Insurance and Insurance Australia positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if QBE Insurance position performs unexpectedly, Insurance Australia 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 Insurance Australia will offset losses from the drop in Insurance Australia's long position.QBE Insurance vs. SALESFORCE INC CDR | QBE Insurance vs. Axcelis Technologies | QBE Insurance vs. Uber Technologies | QBE Insurance vs. CODERE ONLINE LUX |
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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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