Correlation Between Render Network and Golem Network
Can any of the company-specific risk be diversified away by investing in both Render Network and Golem Network 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 Render Network and Golem Network into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Render Network and Golem Network Token, you can compare the effects of market volatilities on Render Network and Golem Network 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 Render Network with a short position of Golem Network. Check out your portfolio center. Please also check ongoing floating volatility patterns of Render Network and Golem Network.
Diversification Opportunities for Render Network and Golem Network
0.77 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Render and Golem is 0.77. Overlapping area represents the amount of risk that can be diversified away by holding Render Network and Golem Network Token in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Golem Network Token and Render Network 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 Render Network are associated (or correlated) with Golem Network. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Golem Network Token has no effect on the direction of Render Network i.e., Render Network and Golem Network go up and down completely randomly.
Pair Corralation between Render Network and Golem Network
Assuming the 90 days trading horizon Render Network is expected to under-perform the Golem Network. In addition to that, Render Network is 1.17 times more volatile than Golem Network Token. It trades about -0.12 of its total potential returns per unit of risk. Golem Network Token is currently generating about -0.08 per unit of volatility. If you would invest 37.00 in Golem Network Token on December 30, 2024 and sell it today you would lose (12.00) from holding Golem Network Token or give up 32.43% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Render Network vs. Golem Network Token
Performance |
Timeline |
Render Network |
Golem Network Token |
Render Network and Golem Network Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Render Network and Golem Network
The main advantage of trading using opposite Render Network and Golem Network positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Render Network position performs unexpectedly, Golem Network 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 Golem Network will offset losses from the drop in Golem Network's long position.Render Network vs. Render Token | Render Network vs. Staked Ether | Render Network vs. Phala Network | Render Network vs. EigenLayer |
Golem Network vs. Staked Ether | Golem Network vs. Phala Network | Golem Network vs. EigenLayer | Golem Network vs. EOSDAC |
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 File Import module to quickly import all of your third-party portfolios from your local drive in csv format.
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