How to evaluate the Cardan's Correlation (ADA): Deep Dive
The world of cryptocurrency is known for its high volatility and rapid price fluctuations. One way to navigate on the market is to evaluate the correlation between different assets, including Cardano (ADA). In this article, we will examine how the Ada market correlation is evaluated using different methods.
What is market correlation?
The correlation of the market concerns the degree of relationship or similarity between the prices of two or more financial instruments over time. This is a way to measure the range in which your movements are synchronized. When two assets move together, it is considered highly correlated. If you differ significantly, it correlates as low.
Cardano (Ada) Characteristics
Before we dive into correlation analysis, we briefly read the most important features of Cardan:
* token price : ADA is a native cryptocurrency network Cardano.
* Market capitalization : Since March 2023, Cardano has a market capitalization of approximately $ 1.4 billion.
* Volume : The volume of ADA trade is significantly with a daily average of more than $ 100 million.
Methods to evaluate market correlation
We will use three common methods to evaluate the Ada market correlation:
1.
Function for automatic function (ACF) : This feature examines how the price of the return correlates with itself and other previous values in the time series data.
Partial autocorrelation function (PACF) : This method offers a more detailed image of relationships between different assets that allow better identification of interactions.
Kovarianz's analysis 
We will use historical data from Cryptocompare to calculate the correlation coefficient between the price of ADA and other cryptocurrencies:
- Ethereum classic (etc.): Digital currency with market capitalization near ADA.
- EOS: Decentralized operating system with relatively high volatility.
- Solana (SOL): Fast, scalable blockchain platform.
With these data records, we can calculate the correlation coefficient based on the following formula:
ρ = σ [(x - μx) (y - μy)] / (δσ (x - μx)^2 \* σ (y - μy)^2)
If there is a correlation coefficient, X represents the price of ADA and Y, the price of the financial value represents each other.
Interpretation of results
The results show exactly how the ADA and its neighboring cryptocurrency prices meet over time. High positive correlation shows that both assets tend to increase or lose weight at a similar speed, while low negative correlation suggests that they differ significantly.
Here is an example of what we could see for every couple:
| Assetum | Correlation coefficient
| --- | --- |
| Ada (x) against etc. (Y) | 0.95 (high positive correlation)
| Ada (x) against EOS (Z) -0.85 (low negative correlation)
| Ada (x) against Sol (W) 0.78 (moderate positive correlation)
Autocorelation Autocorelation Partial function
**
For analysis we can use ACF and PACF to comprehensively understand the relationships between ADA:
- Autocorelation function: This examines how the price of individual assets correlates with it and other previous values in the time series data.
- Partial autocororeration function (PACF): This method offers a more detailed image of the relationship between different assets and allows better interaction identification.
These features can help identify basic formulas and trends that may not recognize a simple correlation analysis. For example::
- The high positive value of PACF shows that the ADA price tends to increase synchronicity with the prices of other assets.