Why do Bitcoin and Altcoins Change Between Inverse and Direct Relationship in Price?

It is all about the concept of market volatility. All currencies on the market have mutual factors that influence their price on one way or another – positive effect or negative. Also, the impact is made by the fiat currencies as well. Every currency ever is based on the value of another one – they come in pair which means that there is already a strong connection considering their value and price. Basically, they are very dependent on one to another. The measure of their connection is the strength of the correlation between them.

Correlation is the measure of mutual effect of the two phenomena. It basically shows the sum of the mutual involvement which represents the strength of their connection mentioned above. Correlation can be negative or positive depending on the mutual factors and their influence on the two currencies at stake. Correlation coefficient can go from -1 to +1. For example, if Bitcoin is on the rise, then people will sell alternative cryptocurrencies in order to buy Bitcoin. As the supply of altcoins increases, the price of altcoins will drop.

How Negative Correlation Works

As the demand of Bitcoin rises, Bitcoin will get more expensive and with that people will want to buy more Bitcoin and possibly earn on the base of day-trading, which although it is very unsafe way of earning remained one of the most popular ones. That is how negative correlation works.

How Positive Correlation Works

A recent example is the positive correlation between cryptocurrencies and government regulations. Global demand for cryptocurrencies drops, when government regulations are placed or when a government official makes a anti-cryptocurrency statement. The government regulations act as a mutual influence factor that move in the same direction. That is how positive correlation works.

That is some basic understanding of mutual involvement of relationship between currencies, there is more comprehensive analysis that can show how and at what extent some other factors influence. Factor analysis is more complex but shows more useful information considering factor saturation per variables (in this case per currency) and it can show causation – which phenomenon influences which, while correlation shows only the interdependence.


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