How to evaluate the corpse correlation with Cardano (Ada): Deep Destroy
The world of cryptocurrency is known for its high volatility and fast prices fluctuations. One way to move on the market is to assess correlation between different assets, including Cardano (ADA). In this article, we will explore how to evaluate the correlation of the market with Ada using different methods.
What is a market correction?
Market correlation refers to the degree of relationship or the like between the price of two or more financial instruments over time. This is a way to determine the extent to which their movements are synchronized. When two property moves together in tandem, it is considered high correlation; When significantly diverging, it is considered low correlation.
Characteristics Cardano (ADA)
Before we dive into a correlation analysis, let’s briefly review Cardan’s key characteristics:
* The price token : Ada is the original Crydano network curve.
* Market Capitalization : Since March 2023. Cardano has a market capitalization of about $ 1.4 billion.
* Volume : Ada trading volume is significant, with a daily average of over $ 100 million.
Methodologies to assess market correlation
To evaluate the correlation of the market from ADA, we will use three usual methodologies:
- Covarian analysis : This method calculates a correlation coefficient between the price of two assets by analyzing their historical prices.
- Autocorelation function (ACF) : This function examines that the price of each property returns to correlation with itself and other previous values in the time series data.
3
Covarian analysis
We will use the historical data from Cryptocompare to calculate the correlation coefficient between Ada’s price and other cryptocurrencies:
- Ethereum Classic (etc.): Digital currency with market capitalization close to that of Ada.
- EOS: a decentralized operating system with relatively high volatility.
- Solana (salt): fast, scalable blockchain platform.
With these data sets, we can calculate the correlation coefficient using the following formula:
ρ = σ [(x – µx) (y – μy)] / (√σ (x – µx)^2 \* √σ (y – μy)^2)
Where ρ is a correlation coefficient, X represents ada -in price, and y represents each other’s price prices.
Interpretation of the results
The results will indicate how much the price of Ada and its adjacent crypto currencies are moving together over time. High positive correlation indicates that both assets increase or decrease at a similar speed, while low negative correlation suggests that they diverge significantly.
Here’s an example of what we could see for every couple:
| Property | Correlation coefficient |
| — | — |
| Ada (x) Vs. etc (y) | 0.95 (high positive correlation)
| Ada (x) Vs. Eos (z) | -0.85 (low negative correlation) |
| Ada (x) against salt (w) | 0.78 (medium positive correlation)
Autocorrelation function and partial autocorelation function
For a comprehensive understanding of the relationship between Ada’s prices, we can use ACF and PACF for analysis:
- Autocorulation function: This examines that the price of each property returns to correlation with itself and other previous values in the time series.
- Partial Autocorulation function (PACF): This method provides a more detailed picture of relationships between different assets, allowing better identification of interactions.
These functions can help identify fundamental patterns and trends that may not be visible from simple correlation analysis. For example:
- High positive PACF value indicates that the price of Ada -a increases in synchronization with the prices of other assets.