Correlation Between IShares ESG and BNY Mellon
Can any of the company-specific risk be diversified away by investing in both IShares ESG and BNY Mellon 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 IShares ESG and BNY Mellon into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between iShares ESG Aggregate and BNY Mellon Core, you can compare the effects of market volatilities on IShares ESG and BNY Mellon 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 IShares ESG with a short position of BNY Mellon. Check out your portfolio center. Please also check ongoing floating volatility patterns of IShares ESG and BNY Mellon.
Diversification Opportunities for IShares ESG and BNY Mellon
1.0 | Correlation Coefficient |
No risk reduction
The 3 months correlation between IShares and BNY is 1.0. Overlapping area represents the amount of risk that can be diversified away by holding iShares ESG Aggregate and BNY Mellon Core in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BNY Mellon Core and IShares ESG 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 iShares ESG Aggregate are associated (or correlated) with BNY Mellon. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BNY Mellon Core has no effect on the direction of IShares ESG i.e., IShares ESG and BNY Mellon go up and down completely randomly.
Pair Corralation between IShares ESG and BNY Mellon
Given the investment horizon of 90 days IShares ESG is expected to generate 1.25 times less return on investment than BNY Mellon. In addition to that, IShares ESG is 1.1 times more volatile than BNY Mellon Core. It trades about 0.04 of its total potential returns per unit of risk. BNY Mellon Core is currently generating about 0.06 per unit of volatility. If you would invest 4,180 in BNY Mellon Core on August 29, 2024 and sell it today you would earn a total of 18.00 from holding BNY Mellon Core or generate 0.43% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
iShares ESG Aggregate vs. BNY Mellon Core
Performance |
| Timeline |
| iShares ESG Aggregate |
| BNY Mellon Core |
IShares ESG and BNY Mellon Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with IShares ESG and BNY Mellon
The main advantage of trading using opposite IShares ESG and BNY Mellon positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if IShares ESG position performs unexpectedly, BNY Mellon 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 BNY Mellon will offset losses from the drop in BNY Mellon's long position.| IShares ESG vs. iShares ESG 1 5 | IShares ESG vs. iShares ESG USD | IShares ESG vs. iShares ESG Aware | IShares ESG vs. iShares ESG Aware |
| BNY Mellon vs. BNY Mellon Large | BNY Mellon vs. BNY Mellon International | BNY Mellon vs. BNY Mellon High | BNY Mellon vs. BNY Mellon ETF |
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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