Correlation Between Ginnie Mae and High Income
Can any of the company-specific risk be diversified away by investing in both Ginnie Mae and High Income 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 Ginnie Mae and High Income into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Ginnie Mae Fund and High Income Fund, you can compare the effects of market volatilities on Ginnie Mae and High Income 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 Ginnie Mae with a short position of High Income. Check out your portfolio center. Please also check ongoing floating volatility patterns of Ginnie Mae and High Income.
Diversification Opportunities for Ginnie Mae and High Income
-0.12 | Correlation Coefficient |
Good diversification
The 3 months correlation between Ginnie and High is -0.12. Overlapping area represents the amount of risk that can be diversified away by holding Ginnie Mae Fund and High Income Fund in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on High Income Fund and Ginnie Mae 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 Ginnie Mae Fund are associated (or correlated) with High Income. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of High Income Fund has no effect on the direction of Ginnie Mae i.e., Ginnie Mae and High Income go up and down completely randomly.
Pair Corralation between Ginnie Mae and High Income
Assuming the 90 days horizon Ginnie Mae Fund is expected to generate 2.31 times more return on investment than High Income. However, Ginnie Mae is 2.31 times more volatile than High Income Fund. It trades about 0.18 of its potential returns per unit of risk. High Income Fund is currently generating about 0.14 per unit of risk. If you would invest 883.00 in Ginnie Mae Fund on September 14, 2024 and sell it today you would earn a total of 9.00 from holding Ginnie Mae Fund or generate 1.02% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Ginnie Mae Fund vs. High Income Fund
Performance |
| Timeline |
| Ginnie Mae Fund |
| High Income Fund |
Ginnie Mae and High Income Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Ginnie Mae and High Income
The main advantage of trading using opposite Ginnie Mae and High Income positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Ginnie Mae position performs unexpectedly, High Income 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 High Income will offset losses from the drop in High Income's long position.| Ginnie Mae vs. Inflation Adjusted Bond Fund | Ginnie Mae vs. Government Bond Fund | Ginnie Mae vs. Income Growth Fund | Ginnie Mae vs. Equity Growth Fund |
| High Income vs. High Yield Municipal Fund | High Income vs. Diversified Bond Fund | High Income vs. Ginnie Mae Fund | High Income vs. Utilities Fund Investor |
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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