“Yes, we still depend on it” — on the price correlation between BTC and other cryptocurrencies

Xena Exchange
Xena Exchange
Published in
4 min readJun 27, 2019

Where Bitcoin goes, altcoins follow — any trader who watches the crypto market knows this. Bitcoin’s explosive growth in late 2017 and an equally dramatic nosedive in 2018, didn’t go unnoticed for ETH, Ripple, Litecoin, and other assets. Following Bitcoin, they set record prices — only to plummet to new lows soon after. In this article we explore the nature of this dependency on Bitcoin, how the correlation works, and how it can be utilized in trading.

Positive and negative correlation

Let’s start with two common forms of correlation movements; positive, in which both variables move in the same direction, and negative, when one variable increases as the other decreases. In the crypto market, correlations tend to be positive, with; altcoins following behind Bitcoin with a certain lag.

Below you can see an example of how BTC, ETH (the second most popular cryptocurrency), and XMR (a top-20 coin) have behaved across different time frames.

It’s easy to see that both ETH and XMR follow the same pattern as BTC, but with a lag.While BTC reached its record price on December 17, 2017, Ethereum did so on January 14 2018, and Monero on January 7.

Positive correlation is often present not only within long time frames but also in the short term; i.e. during periods of high volatility, most cryptocurrencies will fall or rise together. An exception to this pattern is growth after a long flat period — as it happened in April 2019, when XMR saw growth on March 31, and ETH on April 2, while BTC only bounced back on April 4.

Although general correlation trends are clear, some assets are more sensitive to changes in market conditions than others. For instance, both Monero and Ethereum have shown stronger reactions than BTC, with Monero being more volatile than Ethereum. Ripple, by contrast, has demonstrated less sensitivity, reflected by its smooth peaks and troughs compared to BTC after a period of rapid growth in early 2018.

The chart below illustrates the volatility of XRP and BTC between December 10, 2017 and April 4, 2018:

These charts reflect how, beginning from January 22, 2018, XRP has seen less volatility compared to BTC.

The strength of correlation is represented by values between -1 and +1. Hence, +1 means a perfect positive correlation, 0 indicates an absence of association, and -1 denotes a perfect negative correlation.

Taking into account price correlation when balancing a crypto portfolio

Understanding the degree to which different cryptocurrencies correlate is critical for building a balanced portfolio. If one chooses assets whose prices strongly correlate, the portfolio’s value will thus grow or fall together depending on the market. It will therefore not be possible to compensate for these losses with gains on other assets.

It’s also worth pointing out that the entire crypto market reflects certain negative correlations with a number of traditional measures, such as the volatility index VIX and S&P500, as shown in the table above. Crypto experts believe that international large capital are viewing Bitcoin as a security net — the increase of liquidity into the crypto markets represent a means of escaping traditional market risks. As global growth rates fall, investors are looking for instruments which will assist them in waiting out the slow period. These safe-haven assets include gold, fiat currencies from countries least dependent on fossil fuels (such as the Japanese Yen and the Swiss Franc), and now, it appears Bitcoin and other cryptocurrencies are joining that list.

The original story was published on the official Xena Exchange blog

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All indicators, studies, and trading signals provided on the platform are based on technical analysis and are predefined algorithms that use the history of prices, the state of the order book, and other data as input. These tools are to only be used along with thorough market analysis. No tools can guarantee future profits or predict the movement of markets with absolute precision.

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