How to evaluate the market connection with Cardano (Ada): Deep Destroy
The world of cryptocurrency is known for its high volatility and fast prices fluctuations. One way to move the market is an assessment of connection between different assets, including Cardano (Ada). In this article, we will examine how the correction of the market from ADA is estimated by different methods.
What is the market correction?
Market correlation refers to the degree of relationship or resemblance between the price of two or more financial instruments over time. This is a way to measure the scope in which your movements are synchronized. When two property moves together, it is considered very correlated. If you differ significantly, it is correlated as low.
Characteristics Cardano (ADA)
Before we plunge into a correlation analysis, we briefly read the most important features of Cardano:
* Award token : Ada is the original crypto of the Cardano network currency.
* Market Capitalization : Since March 2023. Cardano has a market capitalization of about $ 1.4 billion.
* Volume
: ADA’s volume is significant with a daily average of over $ 100 million.
METHODS FOR ASSEMBLY ASSEMBLY OF MARKET
To evaluate the market connection with Ada, we will use three usual methods:
- Covarian analysis
: This method calculates correlation coefficients between two property prices analyzing their historical prices.
- Autocorelation function (ACF) : This function examines how the price returns to correlation with itself and other previous values in the time series data.
3
Covarianz’s analysis
We will use the historical data from Cryptocompare to calculate the correlation coefficient between the price of Ada and other crypto currency:
- Ethereum Classic (etc.): Digital currency with market capitalization near Ada.
- EOS: a decentralized operating system with relatively high volatility.
- Solana (salt): fast, scalable blockchain platform.
With these records of data, 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 a 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 exactly that the prices of Ada and its adjacent crypto currency gather over time. High positive correlation shows that both assets tend to increase or weight loss at a speed, while low negative correlation indicates that they differ significantly.
Here’s an example of what we could see for every couple:
| Property | 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 salt (w) | 0.78 (medium positive correlation)
Autocorrelation function and partial autocorelation function
ACF and PACF can be used for analysis for a comprehensive understanding of the relationship between Ada:
- Autocorulation function: This examines that the price of individual assets correlate with itself and other previous values in the time series data.
- Partial Autocorulation function (PACF): This method offers a more detailed picture of relationships between different assets and allows for better identification of interactions.
These functions can help identify fundamental patterns and trends that cannot be recognized from simple correlation analysis. For example:
- High positive PACF value shows that the price of Ada is more difficult to increase synchronism with the prices of other assets.
