Correlation Between Bitcoin and SKALE Network
Can any of the company-specific risk be diversified away by investing in both Bitcoin and SKALE 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 Bitcoin and SKALE Network into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bitcoin and SKALE Network, you can compare the effects of market volatilities on Bitcoin and SKALE 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 Bitcoin with a short position of SKALE Network. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bitcoin and SKALE Network.
Diversification Opportunities for Bitcoin and SKALE Network
0.8 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Bitcoin and SKALE is 0.8. Overlapping area represents the amount of risk that can be diversified away by holding Bitcoin and SKALE Network in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on SKALE Network and Bitcoin 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 Bitcoin are associated (or correlated) with SKALE Network. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of SKALE Network has no effect on the direction of Bitcoin i.e., Bitcoin and SKALE Network go up and down completely randomly.
Pair Corralation between Bitcoin and SKALE Network
Assuming the 90 days trading horizon Bitcoin is expected to generate 1.95 times less return on investment than SKALE Network. But when comparing it to its historical volatility, Bitcoin is 1.92 times less risky than SKALE Network. It trades about 0.26 of its potential returns per unit of risk. SKALE Network is currently generating about 0.26 of returns per unit of risk over similar time horizon. If you would invest 3.08 in SKALE Network on September 1, 2024 and sell it today you would earn a total of 4.94 from holding SKALE Network or generate 160.39% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Bitcoin vs. SKALE Network
Performance |
Timeline |
Bitcoin |
SKALE Network |
Bitcoin and SKALE Network Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bitcoin and SKALE Network
The main advantage of trading using opposite Bitcoin and SKALE Network positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bitcoin position performs unexpectedly, SKALE 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 SKALE Network will offset losses from the drop in SKALE Network's long position.The idea behind Bitcoin and SKALE Network 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.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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