How to evaluate the correlation of the market with cardan (ADA): a deep dive
The world of cryptocurrencies is known for high volatility and rapid price fluctuations. One way to browse the market is by evaluating the 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 market correlation?
Market correlation refers to the degree of relationship or resemblance between two or more prices of financial instruments over time. It is a way to measure the measure to which their movements are synchronized. When two assets move together in tandem, it is considered high correlated; When it is significantly diverted, it is considered a low correlated correlated.
Cardano (ADA) Features
Before we dive into the analysis of the correlation, let’s briefly review the key features of Cardano:
* The price token : Ada is cryptocurrency native of the cardan network.
* market capitalization : In March 2023, Cardano has a market capitalization of about $ 1.4 billion.
* Volume
: The Ada trading volume is significant, with a daily average of over $ 100 million.
Methodologies for market correlation assessment
To assess market correlation with Ada, we will use three common methodologies:
- Covement analysis
: This method calculates the coefficient of correlation between the prices of two assets, analyzing their historical price movements.
- The self -co -coach function (ACF) : This function examines the way in which the return of the prices of each asset is correlated with itself and with other previous values in the data series.
- Partial self -co -coach function (PACF) : This method offers a more detailed image of the relationships between different assets, allowing a better identification of interactions.
Covaria analysis
We will use historic data from Cryptocompare to calculate the correlation coefficient between Ada price and other cryptocurrencies:
- Ethereum Classic (etc): a digital currency with a market capitalization close to Ada.
- EOS: a decentralized operating system with relatively high volatility.
- Sola (soil): a quick, scalable blockchain platform.
Using 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)
If ρ is the correlation coefficient, X represents the price of Ada, and Y represents each other the price of the asset.
Interpretation of results
The results will indicate how closely the prices of Ada and neighboring cryptocurrencies move together over time. A high positive correlation indicates that both assets tend to grow or decrease at a similar speed, while a low negative correlation suggests that they are significantly diverting.
Here is an example of what we could see for each pair:
| Active | 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 (average positive correlation) |
Self -co -correlation function and partial self -correlation function
For a more comprehensive understanding of the relationships between Ada prices, we can use ACF and PACF to analyze:
- The self -co -reaction function: it examines the way in which the return of the prices of each asset correlates with itself and with other previous values in the data series data.
- Partial function of self -correlation (PACF): This method offers a more detailed image of relationships between different assets, allowing a better identification of interactions.
These functions can help identify the basic models and trends that may not be obvious from the simple analysis of the correlation. For example:
- A high positive PACF value indicates that the price of Ada tends to increase in synchronization with the prices of other assets.
