Correlation Between Muenchener Rueckver and Hannover
Can any of the company-specific risk be diversified away by investing in both Muenchener Rueckver and Hannover 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 Muenchener Rueckver and Hannover into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Muenchener Rueckver Ges and Hannover Re, you can compare the effects of market volatilities on Muenchener Rueckver and Hannover 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 Muenchener Rueckver with a short position of Hannover. Check out your portfolio center. Please also check ongoing floating volatility patterns of Muenchener Rueckver and Hannover.
Diversification Opportunities for Muenchener Rueckver and Hannover
0.91 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Muenchener and Hannover is 0.91. Overlapping area represents the amount of risk that can be diversified away by holding Muenchener Rueckver Ges and Hannover Re in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Hannover Re and Muenchener Rueckver 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 Muenchener Rueckver Ges are associated (or correlated) with Hannover. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Hannover Re has no effect on the direction of Muenchener Rueckver i.e., Muenchener Rueckver and Hannover go up and down completely randomly.
Pair Corralation between Muenchener Rueckver and Hannover
Assuming the 90 days horizon Muenchener Rueckver Ges is expected to generate 1.13 times more return on investment than Hannover. However, Muenchener Rueckver is 1.13 times more volatile than Hannover Re. It trades about 0.01 of its potential returns per unit of risk. Hannover Re is currently generating about -0.06 per unit of risk. If you would invest 1,086 in Muenchener Rueckver Ges on September 19, 2024 and sell it today you would earn a total of 6.00 from holding Muenchener Rueckver Ges or generate 0.55% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Muenchener Rueckver Ges vs. Hannover Re
Performance |
Timeline |
Muenchener Rueckver Ges |
Hannover Re |
Muenchener Rueckver and Hannover Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Muenchener Rueckver and Hannover
The main advantage of trading using opposite Muenchener Rueckver and Hannover positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Muenchener Rueckver position performs unexpectedly, Hannover 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 Hannover will offset losses from the drop in Hannover's long position.Muenchener Rueckver vs. Swiss Re AG | Muenchener Rueckver vs. SiriusPoint | Muenchener Rueckver vs. Renaissancere Holdings | Muenchener Rueckver vs. Maiden Holdings |
Hannover vs. Maiden Holdings | Hannover vs. Renaissancere Holdings | Hannover vs. Greenlight Capital Re | Hannover vs. Reinsurance Group of |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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