Correlation Between GRIN and Polygon Ecosystem
Can any of the company-specific risk be diversified away by investing in both GRIN and Polygon Ecosystem 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 GRIN and Polygon Ecosystem into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between GRIN and Polygon Ecosystem Token, you can compare the effects of market volatilities on GRIN and Polygon Ecosystem 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 GRIN with a short position of Polygon Ecosystem. Check out your portfolio center. Please also check ongoing floating volatility patterns of GRIN and Polygon Ecosystem.
Diversification Opportunities for GRIN and Polygon Ecosystem
-0.1 | Correlation Coefficient |
Good diversification
The 3 months correlation between GRIN and Polygon is -0.1. Overlapping area represents the amount of risk that can be diversified away by holding GRIN and Polygon Ecosystem Token in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Polygon Ecosystem Token and GRIN 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 GRIN are associated (or correlated) with Polygon Ecosystem. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Polygon Ecosystem Token has no effect on the direction of GRIN i.e., GRIN and Polygon Ecosystem go up and down completely randomly.
Pair Corralation between GRIN and Polygon Ecosystem
Assuming the 90 days trading horizon GRIN is expected to under-perform the Polygon Ecosystem. But the crypto coin apears to be less risky and, when comparing its historical volatility, GRIN is 1.78 times less risky than Polygon Ecosystem. The crypto coin trades about -0.19 of its potential returns per unit of risk. The Polygon Ecosystem Token is currently generating about -0.08 of returns per unit of risk over similar time horizon. If you would invest 116.00 in Polygon Ecosystem Token on January 30, 2024 and sell it today you would lose (43.00) from holding Polygon Ecosystem Token or give up 37.07% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
GRIN vs. Polygon Ecosystem Token
Performance |
Timeline |
GRIN |
Polygon Ecosystem Token |
GRIN and Polygon Ecosystem Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with GRIN and Polygon Ecosystem
The main advantage of trading using opposite GRIN and Polygon Ecosystem positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GRIN position performs unexpectedly, Polygon Ecosystem 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 Polygon Ecosystem will offset losses from the drop in Polygon Ecosystem's long position.The idea behind GRIN and Polygon Ecosystem Token pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.Polygon Ecosystem vs. Solana | Polygon Ecosystem vs. XRP | Polygon Ecosystem vs. Staked Ether | Polygon Ecosystem vs. The Open Network |
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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