How to assess market correlation with Cardano (ADA): Deep diving
The world of cryptocurrencies is known for high variability and fast price fluctuations. One way to move around the market is the assessment of correlation between various assets, including Cardano (ADA). In this article, we will examine how to assess market correlation with ADA, use variable methods.
** What is market correlation?
Market correlation refers to the degree of relationship or similarity between the prices of two or more financial instruments on time. It is a way to assess the degree to which their movements are synchronized. When two assets move together in tandem, it is considered highly associated; When they diverge significantly, she consults her.
Cardano (ada) characteristics
Before we delve into the correlation analysis, let’s get acquainted with the key features of Cardano:
* Token price : ADA is a native cryptocurrency of the Cardano network.
Market capitalization : Until or March 2023, Cardano has a market capitalization of around USD 1.4 billion.
* Volume : ADA volume is significant, with an average or $ 100 million.
Methodologies for assessing market correlation
To assess market correlation with ADA, we will use three common methodologies:
- Analysis Covariacene : This method calculates the correlation factor between the prices of two assets by analyzing their historical price movements.
- Auto -relief function (ACF) : This function studies how the price returns of each resource correlate with each other and other previous values in given time ranks.
- Partial autocorrelation function (PACF) : This method provides a more detailed picture of the relationship between different resources, enabling better identification of interaction.
Covariacene analysis
We will use historical data from Cryptocomomomomomomomomomomomomomomomomomomomomomomomomomom Socity agent between the ADA price and other cryptocurrencies:
- Ethereum Classic (etc.): Digital currency with market capitalization similar to ADA.
- EOS: Decentralized operating system with relatively high variability.
- Solana (SOL): Fast, scalable blockchain platform.
When using work sets, we can calculate the correlation factor to use the following formula:
ρ = σ [(x – μx) (y – μy)] / (√k (x – μx)^2 * √k (y – μy)^2)
Where ρ is a correlation coefficient, X represents the price of Ada, and y represents the mutual price of assets.
Interpretation of the results
The results indicate how close the price of Ada and its neighboring cryptocurrencies move together on time. High positive correlation indicates that both assets tend to grow or decrease at similar speed, while low negative correlation suggests that they will diverge significantly.
Here is an example of what we can see for every pair:
|. Assets Correlation coefficient
|. — | — |
|. Ada (x) vs. Etc. (y) 0.95 (high positive correlation)
|. Ada (x) vs. EOS (z) | -0.85 (low negative correlation)
|. Ada (x) vs. SOL (W) 0.78 (Average positive correlation)
Auto -reaction function and partial autocorrelation function
To get a more comprehensive understanding of the relationship between ADA prices, we can use ACF and PACF for analysis:
- Autocorrelation function: it examines how the returns from the price of each resource correlate with each other and other previous values in given time ranks.
- Partial autocorrelation function (PACF): This method contains a more detailed picture of the relationship between various assets, enabling better identification of interaction.
These functions can help identify basic patterns and trends that may not be visible from a simple correlation analysis. For example:
- High positive PACF value indicates that the price of Ada has a tendency to increase synchronization with the prices of other assets.
