Analytica Data Science SolutionsContact
ESC

to move to open

← All insights

Insight

Analyzing crypto markets in R, part 2: everything correlates

The diversification that wasn't: daily returns for eight coins, correlation matrices, and what happens to independence when markets fall.

December 2018RFinanceData analysis

The 2018 companion piece to part 1, kept for the method and the findings. Its data feeds are gone, so the code is illustrative; the figures are the original outputs.

Part 1 asked whether Bitcoin was one price across exchanges. This half asks a portfolio question: were the altcoins genuinely different assets, or eight tickers for the same bet?

The 2018 altcoin pitch leaned on diversification — don’t just hold BTC, spread across ETH, LTC, XRP and friends. Whether that spreads risk depends entirely on correlation, and correlation is measurable. Same toolkit as before, prices for eight major coins, everything converted to daily returns:

Daily percentage returns for eight coin pairs, 2015–2018: quiet stretches punctuated by synchronized spikes across every panel at once.

Even before any statistics, your eye catches it: the spikes line up. When one panel goes wild, they all do. The correlation matrix makes it precise:

Correlation matrix of daily returns for XRP, XEM, ETH, XMR, LTC, DASH, BCH and BTC: almost everything between 0.7 and 0.95.

Nearly every pair sits between 0.7 and 0.95. In equity terms those are numbers you’d expect within one sector, not across an asset class. Drawn as a network, with distance standing for independence, the “diverse” portfolio collapses into one tight tangle:

Static correlation network, 2014–2018: eight coins knotted into a single cluster, with BCH the only partial outlier.

Eight names, one knot. Holding five of these wasn’t diversification; it was the same position with extra fees.

The part that should worry a portfolio

Averages hide the dangerous detail, so I rolled the BTC–LTC correlation month by month:

Correlation between BTC and LTC daily returns computed per month, 2015–2018: mostly 0.7–0.9, dipping only in quiet periods.

The dips toward independence happen in calm stretches. In stressed ones the correlation pins to the top of the range — and the 2018 price panels show what that meant in practice:

2018 prices for eight coins, faceted: every panel is the same descending shape.

Eight charts, one shape, all pointing down together. Which is the general and much older lesson this data illustrates unusually cleanly: correlations rise exactly when you need them low. Diversification quoted from peaceful averages evaporates in a crisis, in crypto and everywhere else — 2008 taught equity investors the same thing at greater expense.

Would I expect identical numbers today? No; the market has had years to mature, and I’d want fresh data before claiming anything about now. But the method is the reusable part: returns, not prices; correlations over time, not on average; and a hard look at exactly the periods your model is most tempted to smooth over.

Discuss a project

Tell us what decision, workflow, or data problem you're working on. We'll tell you what the data you already have can support, and what a first engagement looks like.

Start a conversation