How To Assess Market Correlation With Cardano (ADA)

How to evaluate the Cardan Market Correlation (ADA): Deep Dive

The world of cryptocurrency is known for its high volatility and rapid price fluctuations. One way to navigate the market is to assess 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?

The correlation of the market concerns the degree of relationship or similarity between two or more prices of financial instruments over time. This is a way to estimate to what extent their movements are synchronized. When two assets move together in tandem, it is considered high correlated; When they differ significantly, this is considered low correlation.

Cardano (Ada) Characteristics

Before we dive into correlation analysis, briefly examine the key characteristics of Cardano:

* token price : ADA is a native cryptocurrency network Cardano.

* Market capitalization

: Since March 2023, Cardano has a market capitalization of approximately $ 1.4 billion.

* Volume

: ADA trading 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 the Ada market correlation:

1.

• Autocorelation function (ACF) : This function examines how the price of each asset is correlated with itself and other previous values ​​in the time series data.

3
Partial autocorrelation function (PACF) : This method provides a more detailed image of relationships between different assets, allowing better interaction identification.

Analysis of Kovariani

We will use historical data from Cryptocompare to calculate the correlation coefficient between the price of ADA and other cryptocurrencies:

• Ethereum Classic (etc.): Digital currency with market capitalization near the ADA currency.

• EOS: Decentralized operating system with relatively high volatility.

• Solana (SOL): Fast, 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 thoroughly the prices of ADA and its 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 every couple:

| Assetum | 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)

Autocorer function and partial autocorrelation function

For a more comprehensive understanding of ADA prices, we can use ACF and PACF for analysis:

• Autocorelation function: This is examined how the price of each price of the asset correlates with itself and other previous values ​​in the time series data.

• Partial autocororeration function (PACF): This method provides a more detailed image of relationships between different assets, allowing better interaction identification.

These features can help identify basic formulas and trends that may not be evident from a simple correlation analysis. For example::

• A high positive PACF value suggests that the AdDA price tends to increase synchronization with prices of other assets.

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