<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Journal of Management and Business Solutions</JournalTitle>
      <Issn></Issn>
      <Volume></Volume>
      <Issue>In Press</Issue>
      <PubDate PubStatus="epublish">
        <Year>2027</Year>
        <Month>07</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Cryptocurrency Market Integration and Systemic Financial Risk: Evidence from Dynamic Spillover Networks</ArticleTitle>
    <VernacularTitle>Cryptocurrency Market Integration and Systemic Financial Risk: Evidence from Dynamic Spillover Networks</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>21</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2026</Year>
        <Month>07</Month>
        <Day>13</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;This study aimed to examine cryptocurrency market integration and systemic financial risk by estimating time-varying dynamic spillover networks among major cryptocurrencies and identifying the principal transmitters and receivers of shocks across different market regimes. This quantitative observational study combined investor-level data from 384 active cryptocurrency investors and traders residing in Tehran with daily market data for Bitcoin, Ethereum, Binance Coin, XRP, Cardano, Solana, Dogecoin, and Litecoin from January 1, 2020, to December 31, 2025. Participant information was collected using a structured questionnaire, while market integration was assessed using daily logarithmic returns. A generalized vector autoregressive framework and generalized forecast-error variance decomposition were applied to estimate total, directional, net, and pairwise spillovers. Dynamic connectedness was examined using rolling-window estimation, and network measures including in-strength, out-strength, eigenvector centrality, and betweenness centrality were used to identify systemically important cryptocurrencies. Robustness was evaluated through alternative lag structures, forecast horizons, and rolling-window lengths. The full-sample total connectedness index was 59.76%, indicating substantial cross-market transmission of return shocks. Bitcoin and Ethereum were the dominant net transmitters, with net spillovers of 38.6 and 24.7, respectively, whereas Litecoin, Dogecoin, Cardano, XRP, Solana, and Binance Coin were net receivers. Dynamic connectedness increased markedly during periods of market stress, reaching a mean of 71.84% during the early-2020 stress period and 76.92% during the 2022 contraction, with a maximum of 89.41%. Bitcoin recorded the highest eigenvector centrality and betweenness centrality, followed by Ethereum. Robustness analyses showed that mean connectedness remained between 57.61% and 61.43% across alternative specifications, while Bitcoin and Ethereum consistently remained net transmitters. Cryptocurrency markets exhibited strong, time-varying systemic integration, with risk transmission intensifying during turbulent periods and concentrating around highly central assets, particularly Bitcoin and Ethereum.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Cryptocurrency</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Market Integration</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Systemic Financial Risk</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Dynamic Spillover Network</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Connectedness</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Bitcoin</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Ethereum</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Financial Contagion</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://journalmbs.com/index.php/jmbs/article/download/478/406</ArchiveCopySource>
  </Article>
</ArticleSet>
