How to evaluate the market correlation with Cardano (ADA): a deep dive
The world of cryptocurrencies is known for its high volatility and fast 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 market correlation with ADA is evaluated using various methods.
What is market correlation?
The market correlation refers to the degree of relationship or the similarity between the prices of two or more financial instruments over time. It is a way to measure the extent in which your movements are synchronized. When two assets move together, it is considered highly correlated. If you differ significantly, it is correlated as low.
Cardano (ada) characteristics
Before we immerse ourselves with the correlation analysis, we briefly read the most important features of Cardano:
* TOKEN Prize : Ada is the native cryptocurrency of the Cardano network.
* Market capitalization : From March 2023, Cardano has a market capitalization of around 1.4 billion US dollars.
* Volume
: The ADA trade volume is significantly with a daily average of over 100 million US dollars.
Methods for evaluating market correlation
In order to evaluate the market correlation with ADA, we will use three common methods:
- Covariance analysis : This method calculates the correlation coefficients between two asset prices by analyzing their historical price movements.
- AutoKorelation function (ACF) : This function examines how the price returns correlate with themselves 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 enable better identification of interactions.
Kovarianz analysis
We will use historical data from CryptoCOMPARE to calculate the correlation coefficient between the price of ADA and other cryptocurrencies:
- Ethereum Classic (etc): A digital currency with a market capitalization near Ada.
- EOS: A decentralized operating system with a relatively high volatility.
- Solana (Sol): A 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)] / (√σ (x – μx)^2 \* √σ (y – μy)^2)
If ρ is the correlation coefficient, X represents the price of ADA and Y, the price of the financial value represents each other.
Interpretation of the results
The results show how exactly the prices of ADA and its neighboring cryptocurrencies come together over time. A high positive correlation shows that both assets tend to increase or lose weight at a similar speed, while a low negative correlation indicates that they differ significantly.
Here is an example of what we could see for every couple:
| Asset | 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 (medium positive correlation) |
AutoKorelation function and partial autocorrelation function **
We can use ACF and PACF for analysis for a more comprehensive understanding of the relationships between ADA:
- The autocorrelation function: This examines how the price of the individual assets correlates with yourself and other previous values in the time series data.
- Partial autocorrelation function (PACF): This method offers a more detailed picture of the relationship between different assets and enables better identification of interactions.
These functions can help identify underlying patterns and trends that may not recognize from a simple correlation analysis. For example:
- A high positive PACF value shows that the price of ADA tends to increase synchronicity with the prices of other assets.
