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How Interconnected Are Cryptocurrencies and What Does This Mean for Risk Management

Author

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  • Filippo Ferroni

Abstract

In the past couple of years, the market for digital currencies, commonly known as cryptocurrencies because transactions are verified using cryptography, has expanded significantly in terms of transaction volumes, market capitalization, and the number of digital currencies in existence. On January 1, 2018, the market capitalizations (market caps1) of Bitcoin and Ethereum were $226 billion and $75 billion, respectively. By May 10, 2021, Bitcoin’s market cap had reached almost $1 trillion and Ethereum’s $478 billion. In this article, I measure the market’s interconnections in term of prices and volatility. Measuring interconnection (or connectedness) is important for both measuring and managing risk. The more the market is connected, the more sensitive it is to shocks. While the cryptocurrency market is not very large relative to the other markets studied in the literature, it is growing at a fast pace and a preliminary assessment might be useful. Moreover, the risk associated with a portfolio of cryptocurrencies is not simply a weighted sum of the risks of its components; rather, the overall risk depends on how the pieces interact—whether and how they are connected. Similarly, in an interconnected market, the risk of a single currency does not depend only on its own idiosyncratic characteristics, but also on the volatility of other currencies and on the extent to which they are interconnected.

Suggested Citation

  • Filippo Ferroni, 2022. "How Interconnected Are Cryptocurrencies and What Does This Mean for Risk Management," Chicago Fed Letter, Federal Reserve Bank of Chicago, issue 466, pages 1-5, March.
  • Handle: RePEc:fip:fedhle:93855
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    Cited by:

    1. Bertsch, Christoph, 2023. "Stablecoins: Adoption and Fragility," Working Paper Series 423, Sveriges Riksbank (Central Bank of Sweden).

    More about this item

    Keywords

    Cryptocurrencies; Market integration; Connectedness and Spillover; VAR; Portfolio Choice; investment decisions; Financial Forecasting and Simulation; Federal Reserve Bank of Chicago; Chicago Fed; Federal Reserve System;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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