Correlation Between Kava and Hivemapper
Can any of the company-specific risk be diversified away by investing in both Kava and Hivemapper 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 Kava and Hivemapper into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Kava and Hivemapper, you can compare the effects of market volatilities on Kava and Hivemapper 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 Kava with a short position of Hivemapper. Check out your portfolio center. Please also check ongoing floating volatility patterns of Kava and Hivemapper.
Diversification Opportunities for Kava and Hivemapper
0.67 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Kava and Hivemapper is 0.67. Overlapping area represents the amount of risk that can be diversified away by holding Kava and Hivemapper in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Hivemapper and Kava 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 Kava are associated (or correlated) with Hivemapper. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Hivemapper has no effect on the direction of Kava i.e., Kava and Hivemapper go up and down completely randomly.
Pair Corralation between Kava and Hivemapper
Assuming the 90 days trading horizon Kava is expected to under-perform the Hivemapper. But the crypto coin apears to be less risky and, when comparing its historical volatility, Kava is 1.43 times less risky than Hivemapper. The crypto coin trades about -0.04 of its potential returns per unit of risk. The Hivemapper is currently generating about -0.02 of returns per unit of risk over similar time horizon. If you would invest 6.28 in Hivemapper on November 27, 2024 and sell it today you would lose (2.03) from holding Hivemapper or give up 32.32% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 98.44% |
Values | Daily Returns |
Kava vs. Hivemapper
Performance |
Timeline |
Kava |
Hivemapper |
Kava and Hivemapper Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Kava and Hivemapper
The main advantage of trading using opposite Kava and Hivemapper positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Kava position performs unexpectedly, Hivemapper 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 Hivemapper will offset losses from the drop in Hivemapper's long position.The idea behind Kava and Hivemapper 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.Hivemapper vs. Staked Ether | Hivemapper vs. Phala Network | Hivemapper vs. EigenLayer | Hivemapper 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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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