understanding the complex world of cryptocurrency market correlation
The world of cryptocurrencies is a complex and rapid development landscape, with numerous cryptocurrencies at different prices. One of the aspects that have observed in recent years is the correlation between various cryptocurrencies. In this article, we deepen in the analysis of the correlation of the market between various cryptocurrencies, providing a perspective on the factors that affect these relationships.
What is cryptocurrency market correlation?
Coriptomonede’s correlation refers to the degree of resemblance or the relationship between two or more cryptocurrency markets. When two or more assets are correlated, it means that their prices change together in response to changes in the price of an asset. This may result from various factors such as:
- Liquidity : high liquidity assets tend to attract more traders and investors, which can lead to increased correlation between markets.
- Price movements
: When facing a significant price movement, it can affect the prices of other assets on its market.
- Square mood : Different factors, such as economic indicators, news and regulatory development, are influenced by cryptocurrency market mood, which can affect correlations.
Factors affecting market correlation
A few factors contribute to the correlation between various cryptocurrencies:
- Liquidity : High liquidity assets tend to attract more traders and investors.
- Price movements : When facing a significant price movement, it can affect the prices of other assets on its market.
- Square mood : Different factors, such as economic indicators, news and regulatory development, are influenced by market moods.
- Regulatory environment : Changes in regulatory media can affect the correlation between cryptocurrency.
Methods of analyzing market correlation
There are several methods of analyzing market correlation between various cryptocurrencies:
- Medium correlation coefficient (MCC) : This is a large -scale method that calculates the average correlation products between each pair of assets and their standard deviations.
- Auto -relief (ACF) function and partial self -co -co -correlation function (PACF) : These methods analyze the relationship between time rankings between different resources, examining the automatic reference function and, respectively, the function of partial self -coach.
- Regression analysis : This method consists of using linear or non -linear regression models to estimate the correlation between two or more assets.
Sample analysis
Let us consider the hypothetical example of the market correlation analysis between Bitcoins (BTC) and Ethereum (ETH).
|. Active range of variation prices
|. — | — | — |
|. BTC $ 2,500 – $ 3000 | 20% – 30% |
|. ETH 150 USD – 200 USD | 50% – 60% |
Using the example above, we can calculate the correlation factor between BTC and ETH using the following formula:
Mcc = (σ (x – x̄) (y – ȳ)) / sqrt (σ (x – x̄) ² \* σ (y – ȳ) ²)
Where x and y are the price of BTC and ETH respectively, and X̄ and ȳ are their means.
After calculating the correlation coefficient (0.95), we can interpret it as follows:
- The correlation factor similar to 1 indicates a strong positive relationship between BTC and ETH.
- The correlation factor similar to -1 indicates a strong negative relationship between BTC and ETH.
- The correlation coefficient of less than or equal to 1 indicates a weak positive relationship, while the value greater than 1 indicates a weak negative relationship.
Application
The correlation of the cryptocurrency market is an important aspect of understanding the complex world of cryptocurrency markets.