Correlation Between Origin Emerging and Deutsche Gnma
Can any of the company-specific risk be diversified away by investing in both Origin Emerging and Deutsche Gnma 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 Origin Emerging and Deutsche Gnma into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Origin Emerging Markets and Deutsche Gnma Fund, you can compare the effects of market volatilities on Origin Emerging and Deutsche Gnma 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 Origin Emerging with a short position of Deutsche Gnma. Check out your portfolio center. Please also check ongoing floating volatility patterns of Origin Emerging and Deutsche Gnma.
Diversification Opportunities for Origin Emerging and Deutsche Gnma
-0.49 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Origin and Deutsche is -0.49. Overlapping area represents the amount of risk that can be diversified away by holding Origin Emerging Markets and Deutsche Gnma Fund in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Deutsche Gnma and Origin Emerging 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 Origin Emerging Markets are associated (or correlated) with Deutsche Gnma. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Deutsche Gnma has no effect on the direction of Origin Emerging i.e., Origin Emerging and Deutsche Gnma go up and down completely randomly.
Pair Corralation between Origin Emerging and Deutsche Gnma
Assuming the 90 days horizon Origin Emerging Markets is expected to generate 1.29 times more return on investment than Deutsche Gnma. However, Origin Emerging is 1.29 times more volatile than Deutsche Gnma Fund. It trades about 0.12 of its potential returns per unit of risk. Deutsche Gnma Fund is currently generating about -0.06 per unit of risk. If you would invest 1,027 in Origin Emerging Markets on October 25, 2024 and sell it today you would earn a total of 18.00 from holding Origin Emerging Markets or generate 1.75% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 86.84% |
Values | Daily Returns |
Origin Emerging Markets vs. Deutsche Gnma Fund
Performance |
Timeline |
Origin Emerging Markets |
Risk-Adjusted Performance
0 of 100
Weak | Strong |
Very Weak
Deutsche Gnma |
Origin Emerging and Deutsche Gnma Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Origin Emerging and Deutsche Gnma
The main advantage of trading using opposite Origin Emerging and Deutsche Gnma positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Origin Emerging position performs unexpectedly, Deutsche Gnma 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 Gnma will offset losses from the drop in Deutsche Gnma's long position.Origin Emerging vs. Payden Government Fund | Origin Emerging vs. Us Government Securities | Origin Emerging vs. Intermediate Government Bond | Origin Emerging vs. Franklin Adjustable Government |
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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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