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arxiv: 2412.19983 · v2 · pith:JQ2COJN3new · submitted 2024-12-28 · 💱 q-fin.RM · stat.AP

A Dynamic Spillover Effect Investigation on Cryptocurrency Market Before and After Pandemic

Wenjie Lan This is my paper

Pith reviewed 2026-05-23 07:22 UTC · model grok-4.3

classification 💱 q-fin.RM stat.AP
keywords cryptocurrencyrisk spilloverCOVID-19asymmetric breakpointcrude oil resonancerisk resonancepandemicnetwork structure
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The pith

The COVID-19 pandemic significantly increased risk spillovers among cryptocurrencies, amplified by resonance with the crude oil market.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper applies a newly developed asymmetric breakpoint approach to separate risk resonance from diversification relationships within cryptocurrency networks. It examines how these risk associations evolved dynamically before and after the pandemic through node associations and network structure. The analysis shows that the outbreak made risk propagation among cryptocurrencies more significant, with rising confirmed cases directly worsening spillover effects. Resonance between the crude oil market and cryptocurrencies extended the pandemic's impact, while other financial markets remained largely independent. The study outlines regulatory strategies for managing cryptocurrency risks during public health crises.

Core claim

The asymmetric breakpoint approach shows that risk propagation among cryptocurrencies becomes more significant under the new crown outbreak, with increases in confirmed cases exacerbating the risk spillover effect while the risk resonance effect between the crude oil market and the cryptocurrency market amplifies the outbreak's impact, and other financial markets remain relatively independent of the cryptocurrency market.

What carries the argument

The asymmetric breakpoint approach, which distinguishes risk resonance from risk diversification relationships and captures the dynamic evolutionary relationship of cryptocurrency risk associations via node and network analysis.

Load-bearing premise

The newly developed asymmetric breakpoint approach correctly distinguishes risk resonance from diversification relationships and accurately captures the dynamic evolutionary relationship of cryptocurrency risk association without methodological biases or data artifacts.

What would settle it

Re-running the analysis on the same cryptocurrency and epidemic data using standard symmetric breakpoint methods that finds no increase in spillover effects after the pandemic onset would falsify the central claim.

Figures

Figures reproduced from arXiv: 2412.19983 by Wenjie Lan.

Figure 1
Figure 1. Figure 1: Correlation Matrix of Cryptocurrencies 3.2 Overall Risk Scoring and Decomposition of Networks To capture the dynamic evolution of overall risk in the cryptocurrency market, this paper calculates the market-wide risk at each time point in the sample period using a market-cap-weighted approach. Observing the trend from [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Cryptocurrency Adjacency Matrix Average A￾BAT BTC BTCCash BNB Dash Dtl Dogcoin EOS Ethereum EC KT Litecoin Loopring Monero NEM Neo Stellar Tether Tezos TN TRON Vechain Waves XRP Zcash -1 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 Average A+ BAT BTC BTCCash BNB Dash Dtl Dogcoin EOS Ethereum EC KT Litecoin Loopring Monero NEM Neo Stellar Tether Tezos TN TRON Vechain Waves XRP Zcash 0 0.1 0.2 0.3 0.4 0.5 … view at source ↗
Figure 3
Figure 3. Figure 3: Average values of the adjacency matrices [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of Network Relationships in from 2019 to 2022 - [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Systemic Risk Scores and the Ratio of Negative Correlations [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
read the original abstract

This paper distinguishes between risk resonance and risk diversification relationships in the cryptocurrency market based on the newly developed asymmetric breakpoint approach, and analyzes the risk propagation mechanism among cryptocurrencies under extreme events. In addition, through the lens of node association and network structure, this paper explores the dynamic evolutionary relationship of cryptocurrency risk association before and after the epidemic. In addition, the driving mechanism of the cryptocurrency risk movement is analyzed in a depth with the epidemic indicators. The findings show that the effect of propagation of risk among cryptocurrencies becomes more significant under the influence of the new crown outbreak. At the same time, the increase in the number of confirmed cases exacerbated the risk spillover effect among cryptocurrencies, while the risk resonance effect that exists between the crude oil market and the cryptocurrency market amplified the extent of the outbreak's impact on cryptocurrencies. However, other financial markets are relatively independent of the cryptocurrency market. This study proposes a strategy to deal with the spread of cryptocurrency risks from the perspective of a public health crisis, providing a useful reference basis for improving the regulatory mechanism of cryptocurrencies.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript develops an asymmetric breakpoint approach to distinguish risk resonance from diversification in cryptocurrency networks and uses it to examine dynamic spillover evolution before and after the COVID-19 pandemic. It further links risk movements to epidemic indicators and compares interactions with crude oil and other financial markets, concluding that post-pandemic risk propagation strengthened, was amplified by case counts and oil resonance, and that other markets remained relatively independent.

Significance. If the asymmetric breakpoint method is shown to be valid and the empirical patterns replicate under standard robustness protocols, the work could add to the literature on crisis-driven network dynamics in crypto markets and supply a public-health lens for risk regulation. The absence of method validation, data transparency, and benchmark comparisons currently prevents assessment of whether these contributions are realized.

major comments (2)
  1. [Methodology] Methodology section: The asymmetric breakpoint approach is presented as newly developed yet lacks any synthetic-data validation, comparison to established spillover estimators such as Diebold-Yilmaz, or tests for breakpoint mis-specification; because every headline claim (increased post-pandemic spillover, case-count exacerbation, oil amplification) rests on this method correctly separating resonance from diversification, the omission is load-bearing.
  2. [Empirical Results] Empirical results and data description: No information is supplied on the cryptocurrency panel (assets, frequency, sample window), the precise epidemic indicators used, or any statistical significance/robustness checks for the reported changes in network structure; without these, it is impossible to evaluate whether the claimed dynamic evolutionary relationships are artifacts of post-hoc breakpoint selection or data artifacts.
minor comments (2)
  1. [Abstract] Abstract: The abstract states conclusions without any reference to data sources, sample period, or method validation, which is atypical for empirical risk-management papers and reduces immediate assessability.
  2. [Figures] Notation and figures: Network diagrams and breakpoint illustrations would benefit from explicit legends indicating resonance versus diversification edges and from reporting the exact parameter values used in the asymmetric procedure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which identify key areas where additional validation and transparency are needed to strengthen the manuscript. We will revise the paper to address both major points.

read point-by-point responses

  1. Referee: [Methodology] Methodology section: The asymmetric breakpoint approach is presented as newly developed yet lacks any synthetic-data validation, comparison to established spillover estimators such as Diebold-Yilmaz, or tests for breakpoint mis-specification; because every headline claim (increased post-pandemic spillover, case-count exacerbation, oil amplification) rests on this method correctly separating resonance from diversification, the omission is load-bearing.

    Authors: We acknowledge that the manuscript does not currently include synthetic-data validation, benchmark comparisons to the Diebold-Yilmaz index, or explicit tests for breakpoint mis-specification. In the revised version we will add a dedicated validation subsection containing Monte Carlo simulations designed to assess the method's ability to separate resonance from diversification, direct comparisons with the Diebold-Yilmaz spillover index on the same cryptocurrency panel, and sensitivity checks for alternative breakpoint specifications. These additions will directly support the headline empirical claims. revision: yes

  2. Referee: [Empirical Results] Empirical results and data description: No information is supplied on the cryptocurrency panel (assets, frequency, sample window), the precise epidemic indicators used, or any statistical significance/robustness checks for the reported changes in network structure; without these, it is impossible to evaluate whether the claimed dynamic evolutionary relationships are artifacts of post-hoc breakpoint selection or data artifacts.

    Authors: We agree that the absence of these details limits evaluation. The revised manuscript will contain an expanded data section that specifies the exact cryptocurrency assets, data frequency, sample window, and sources of the epidemic indicators, together with formal statistical significance tests for the reported network changes and a set of robustness checks (alternative breakpoint dates, subsample periods, and placebo tests) to rule out post-hoc selection artifacts. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation self-contained

full rationale

The abstract introduces a newly developed asymmetric breakpoint approach for distinguishing resonance vs. diversification and analyzing dynamic spillovers, with findings on post-pandemic effects. No equations, parameter-fitting steps, or self-citation chains are quoted that reduce any prediction or distinction to the inputs by construction. The method is presented as external to the data analysis rather than tautological. This is the common honest outcome for an empirical paper whose central claims rest on application of a stated technique rather than internal redefinition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review prevents identification of specific free parameters, axioms, or invented entities. The 'newly developed asymmetric breakpoint approach' may introduce ad hoc elements but cannot be audited.

pith-pipeline@v0.9.0 · 5704 in / 1211 out tokens · 30721 ms · 2026-05-23T07:22:02.907795+00:00 · methodology

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Reference graph

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