Correlation Between FIREWEED METALS and Deutsche Lufthansa
Can any of the company-specific risk be diversified away by investing in both FIREWEED METALS and Deutsche Lufthansa 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 FIREWEED METALS and Deutsche Lufthansa into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between FIREWEED METALS P and Deutsche Lufthansa AG, you can compare the effects of market volatilities on FIREWEED METALS and Deutsche Lufthansa 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 FIREWEED METALS with a short position of Deutsche Lufthansa. Check out your portfolio center. Please also check ongoing floating volatility patterns of FIREWEED METALS and Deutsche Lufthansa.
Diversification Opportunities for FIREWEED METALS and Deutsche Lufthansa
-0.05 | Correlation Coefficient |
Good diversification
The 3 months correlation between FIREWEED and Deutsche is -0.05. Overlapping area represents the amount of risk that can be diversified away by holding FIREWEED METALS P and Deutsche Lufthansa AG in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Deutsche Lufthansa and FIREWEED METALS 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 FIREWEED METALS P are associated (or correlated) with Deutsche Lufthansa. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Deutsche Lufthansa has no effect on the direction of FIREWEED METALS i.e., FIREWEED METALS and Deutsche Lufthansa go up and down completely randomly.
Pair Corralation between FIREWEED METALS and Deutsche Lufthansa
Assuming the 90 days horizon FIREWEED METALS P is expected to generate 1.19 times more return on investment than Deutsche Lufthansa. However, FIREWEED METALS is 1.19 times more volatile than Deutsche Lufthansa AG. It trades about 0.15 of its potential returns per unit of risk. Deutsche Lufthansa AG is currently generating about -0.09 per unit of risk. If you would invest 93.00 in FIREWEED METALS P on October 25, 2024 and sell it today you would earn a total of 5.00 from holding FIREWEED METALS P or generate 5.38% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
FIREWEED METALS P vs. Deutsche Lufthansa AG
Performance |
Timeline |
FIREWEED METALS P |
Deutsche Lufthansa |
FIREWEED METALS and Deutsche Lufthansa Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with FIREWEED METALS and Deutsche Lufthansa
The main advantage of trading using opposite FIREWEED METALS and Deutsche Lufthansa positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if FIREWEED METALS position performs unexpectedly, Deutsche Lufthansa 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 Deutsche Lufthansa will offset losses from the drop in Deutsche Lufthansa's long position.FIREWEED METALS vs. Wayside Technology Group | FIREWEED METALS vs. GMO Internet | FIREWEED METALS vs. ETFS Coffee ETC | FIREWEED METALS vs. Charter Communications |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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