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How to evaluate the correlation of the market with Cardano (Ada): a deep immersion

The world of cryptocurrencies is known for its high volatility and rapid price fluctuations. A way to surf the market is to evaluate the correlation between different activities, including Cardano (Ada). In this article, we will explore how to evaluate market correlation with Ada using various methods.

What is the market correlation?

The correlation of the market refers to the degree of relationship or similarity between two or more prices of financial instruments over time. It is a way to evaluate the measure that their movements are synchronized. When two activities move together in tandem, it is considered highly correlated; When they diverge significantly, it is considered low related.

Cardano (ADA) Features

Before immersing ourselves in the correlation analysis, let’s briefly examine the key characteristics of Cardano:

* Price token : Ada is the native cryptocurrency of the Cardano Network.

* Mercato capitalization : starting from March 2023, Cardano has a market capitalization of about $ 1.4 billion USD dollars.

* Volume

: The volume of trading Ada is significant, with a daily average of over $ 100 million USD.

Methods for evaluation of market correlation

To evaluate the correlation of the market with Ada, we will use three common methodologies:

• Analysis of Covarianza : This method calculates the correlation coefficient between the prices of two activities by analyzing their historical price movements.

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Analysis of Covarianity

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

• Ethereum Classic (etc.): a digital currency with a market capitalization near that of Ada.

• EOS: a decentralized operating system with relatively high volatility.

• Solana (Sol): a fast and scalable blockchain platform.

Using these databases, 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 the correlation coefficient, X represents the price of Ada and Y represents the price of at the one asset.

Interpretation of results

The results will indicate how close the prices of Ada and its nearby cryptocurrencies are moved over time. A high positive correlation indicates that both activities tend to increase or decrease at a similar rhythm, while a low negative correlation suggests that they diverge significantly.

Here is an example of what we could see for each couple:

| Activities | 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 (positive average correlation) |

Function of self -correlation and function of partial self -corner

For a more complete understanding of the relationships between Ada prices, we can use ACF and Pacf to analyze:

• The function of self -correction: this examines the way in which the price of each activity is related to itself and to other previous values ​​in the data of the temporal series.

• Partial self -cornering function (PACF): this method provides a more detailed picture of the relationships between different resources, allowing a better identification of the interactions.

These functions can help identify below models and tendencies that may not be evident from a simple correlation analysis. For example:

• A high positive PACF value indicates that the ADA price tends to increase synchrony with the prices of other activities.