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One Rising Ship Sinks Other Ships: Cross-Chain Negative Spillovers in Crypto Markets

Mengzhong Ma, Te Bao, Yonggang Wen

Abstract

We document the first systematic evidence of negative spillover effects in crypto asset returns across blockchains. Using on-chain data from Ethereum, Solana, Binance Smart Chain, Arbitrum, and Avalanche (2022-2025), we show that surges on one chain often coincide with declines on others, in contrast to the positive co-movements typical of equity markets. These spillovers intensify during attention shocks, proxied by chain activity and extreme return events, and persist after controlling for global equity returns, interest rates, and Bitcoin. Nonlinear factor models reveal that attention-driven capital reallocation, rather than common information, underlies these dynamics. Our findings introduce a new form of cross-market linkage, attention-induced substitution, that shapes risk transmission in crypto markets. The results carry implications for portfolio diversification, systemic risk measurement, and regulation of token launches that may trigger cross-chain capital flight.

One Rising Ship Sinks Other Ships: Cross-Chain Negative Spillovers in Crypto Markets

Abstract

We document the first systematic evidence of negative spillover effects in crypto asset returns across blockchains. Using on-chain data from Ethereum, Solana, Binance Smart Chain, Arbitrum, and Avalanche (2022-2025), we show that surges on one chain often coincide with declines on others, in contrast to the positive co-movements typical of equity markets. These spillovers intensify during attention shocks, proxied by chain activity and extreme return events, and persist after controlling for global equity returns, interest rates, and Bitcoin. Nonlinear factor models reveal that attention-driven capital reallocation, rather than common information, underlies these dynamics. Our findings introduce a new form of cross-market linkage, attention-induced substitution, that shapes risk transmission in crypto markets. The results carry implications for portfolio diversification, systemic risk measurement, and regulation of token launches that may trigger cross-chain capital flight.
Paper Structure (20 sections, 74 equations, 4 figures)

This paper contains 20 sections, 74 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Literature Review and Institutional Background
  3. Related Literature
  4. Institutional Background
  5. Chain Portfolio Construction and Modeling Negative Spillover Effects on Returns
  6. Chain Portfolio Construction
  7. Blockchain Activity and Incentive Metrics
  8. Global Market Variables
  9. Specification of Econometric Model
  10. The linear models
  11. The non-linear models for singal-extraction
  12. Volatility clustering
  13. Empirical Results
  14. Data and Descriptive Statistics
  15. On-Chain Assets
  16. ...and 5 more sections

Figures (4)

  • Figure 1: The conponents and liquidity flow of the crypto market.
  • Figure 2: Number of Issued Assets on Different Chains Over Time. The assets lists for the chains are fetched from https://docs.coingecko.com/reference/introduction and Coingecko filter assets with conditions for assets in their list. Details of the filtering conditions can be found at: https://support.coingecko.com/hc/en-us/articles/4498809321369-Why-is-my-token-not-listed-on-CoinGecko
  • Figure 3: Percentage of CEX-listed Assets on Different Chains Over Time. Dates of CEX-listing for the assets are fetched via https://developers.coindesk.com, and we treat the date when the first trade on any of the largest 100 CEXs happened as the date of CEX-listing.
  • Figure 4: Percentage Assets on Multiple Chains for Different Chains Over Time. We determine the date of issuance of an asset on a chain by the top pool creation date fetched from https://docs.coingecko.com/reference/top-pools-network. Here the percentage of multi-chain assets for $chain_{i}$ is calculated as $\frac{\mathrm{Number\;of\;assets\;also\;exiting\;on\;other\;chains}}{\mathrm{Number\;of\;assets\;actively\;traded\;on\;chain_{i}}}$, so if $chain_i$ is a chain receiving assets from other chains, this percentage could be over 100%. For $Arbitrum$, its percentage before Dec 2023 is larger than 100%, because this chain is a layer-2 chain for Ethereum, receiving and taking over working load from Ethereum.