Paper: arXiv 2410.12801

Authors: Ariston Karagiorgis, Antonis Ballis, Konstantinos Drakos, Christos Kallandranis

Abstract

We examine how skewness interacts with kurtosis within the cryptocurrency market. We show that during the COVID-19 pandemic there are more clusters of observations around the two flanks, highlighting the presence of a volatile behavior. Moreover, we document the evolvement of the interrelationship as the pandemic progresses, identifying the domination of the extremes. Our findings advance the thinking that by exploiting the interrelationship between the two higher moments of cryptocurrencies, investors and researchers can have in their arsenal an additional analytic tool.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 5.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper applies established statistical methodologies for higher moments (skewness and kurtosis) to cryptocurrency data across pre- and post-COVID periods. While mathematically straightforward, the empirical evaluation uses a decent panel of the top 50 cryptocurrencies, though the overall novelty is incremental.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Analyze Skewness & Kurtosis Dynamics in Crypto Markets During COVID-19"] --> B["Data Inputs:<br>Cryptocurrency Market Return Series"]
    B --> C["Methodology:<br>Calculate Higher Moments & Track Temporal Evolution"]
    C --> D["Cluster Analysis:<br>Identify Distribution Flanks & Extreme Patterns"]
    D --> E["Key Finding 1:<br>Increased Observation Clusters Around Flanks Showing Volatile Behavior"]
    D --> F["Key Finding 2:<br>Dominance of Extremes & Evolving Moment Interrelationship"]
    E & F --> G["Outcome:<br>New Analytic Tool Using Moment Interplay for Crypto Investors"]

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0 of 70 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.