A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes

Paper: arXiv 2404.04962

Abstract

The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility. The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers, such as daily leverage, signed volatility and jumps. Several known autoregressive model specifications are estimated over different market regimes, and results are compared to equity data as a reference benchmark of a more mature asset class. The panel estimations show that the positive market returns at the high-frequency level increase price volatility, contrary to what is expected from the classical financial literature. We attributed this effect to the price dynamics over the last year of the dataset (2022) by repeating the estimation on different time spans. Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies’ future volatility, unlike what emerges from the same study on a cross-section of stocks. This result signals a structural difference in a nascent cryptocurrency market that has to mature yet. Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced econometric techniques including realized volatility estimators, panel data regressions, and model specifications over multiple market regimes, demonstrating significant mathematical depth. It is grounded in substantial empirical analysis using high-frequency data from 87 cryptocurrencies (2020-2022) and a cross-section of stocks, with explicit comparisons and statistical significance testing, making it highly data and implementation-heavy.

Research Flowchart

  flowchart TD
  A["Research Goal: Compare Crypto Volatility Drivers<br>vs. Mature Asset Classes"] --> B["Data: High-Frequency Panel Data<br>2020-2022, Crypto & Equity"]

  B --> C["Methodology: Autoregressive Models<br>Panel Estimations over Market Regimes"]

  C --> D{"Computational Analysis"}

  D --> E["Aggregated Panel Results"]
  D --> F["Individual Level Analysis"]

  E --> G["Key Findings & Outcomes"]
  F --> G

  subgraph G ["Structural Differences in Nascent Crypto Market"]
      G1["High-frequency returns INCREASE volatility<br>(Contrary to classical finance literature)"]
      G2["Positive signed vol & negative leverage<br>positively impact future volatility"]
      G3["Effects significant across many assets<br>vs. cross-section of stocks"]
  end

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5. When more pain is preferred to less: Adding a better end (1993)
6. Perceived variance and preference for sequences of outcomes (2012)
7. Social theory at HBS: McGinnis’ two FOs (2004)
8. On the relation between the expected value and the volatility of the nominal excess return on stocks (1993)

0 of 167 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.