How to evaluate the correlation of the cardan market (ADA): Deep Dive
The cryptocurrency world is known for its high volatility and rapid price fluctuations. One way to navigate the market is to evaluate the correlation between different assets, including cardano (ADA). In this article, we will examine how to evaluate the correlation of the ad market using different methods.
What is market correlation?
Market correlation concerns the degree of relationship or similarity between two or more financial instrument prices over time. This is a way of estimating to what extent your movements are synchronized. When two actives move together, it is considered high correlated; When they differ significantly, this is considered low correlation.
Cardano Characteristics (ADA)
Before we deepen the correlation analysis, briefly examine the main characteristics of cardano:
* Token Price : ADA is a cardano native cryptocurrency network.
* Market capitalization
: Since March 2023, Cardano has has a market capitalization of approximately US $ 1.4 billion.
* Volume : ADA negotiation volume is significant, with a daily average of more than $ 100 million.
Methodology to evaluate market correlation
We will use three common methodologies to evaluate ADA market correlation:
1.
2.
3
Partial Autocorrelation Function (PACF) : This method provides a more detailed image of the relationships between different assets, allowing for better interaction identification.
Kovariani Analysis
We will use Cryptocompact historical data to calculate the correlation coefficient between the price of ADA and other cryptocurrencies:
- Classic Ethereum (etc.): Digital currency with market capitalization near the ADA currency.
- EOS: Decentralized operating system with relatively high volatility.
- Solana (Sol): Quick and scalable blockchain platform.
Using these data files, we can calculate the correlation coefficient using the following formula:
ρ = σ [(x – μx) (y – μY)] / (Δσ (x – μx)^2 \* σ (y – μY)^2)
Where ρ is a correlation coefficient, x represents the price of ADA and Y represents the price of an asset with each other.
Interpretation of results
The results indicate how completely ADA prices and their neighboring cryptocurrencies are moving over time. High positive correlation suggests that both assets tend to increase or decrease at a similar speed, while low negative correlation suggests that they differ significantly.
Here is an example of what we could see for each couple:
| Assetm | 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 (moderate positive correlation)
AutoCorem Function and Partial Autocorrelation Function
For a broader understanding of ADA prices, we can use ACF and PACF for analysis:
- Autocorelation Function: This examines how the price of each asset price correlates with oneself and other previous values in the temporal series data.
- Partial Self -Conorreration Function (PACF): This method provides a more detailed image of relationships between different assets, allowing for better interaction identification.
These features can help identify basic formulas and trends that may not be evident from a simple correlation analysis. For example::
- The high positive value of PACF suggests that the price of ADA tends to increase synchronization with the prices of other assets.