Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach

By Qiang Ji, Elie Bouri, Rangan Gupta, David Roubaud. Quarterly Review of Economics and Finance, 2018

241 citations recorded by the source corpus.

Unlike prior studies that have mostly relied on ad hoc network structures, we use a data-driven methodology, namely the directed acyclic graph (DAG), to uncover the contemporaneous and lagged causal relations among Bitcoin and a set of financial assets. The DAG methodology allows the identification of networks of causality based on the observed correlations and partial correlations approach, without making a priori causal assumptions. The main results indicate that the Bitcoin market is quite isolated, especially during its bull market state. We also conduct forecast error variance decompositions and show that the influence of different financial assets on Bitcoin up to the 20-day horizon does not account for more than 10% of innovations in all cases.

Subfields: Economics and Econometrics, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/eaeb64cda0e9dde8b0a7adcda1736118ee463ac1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

  1. Elie Bouri
  2. Rangan Gupta
  3. David Roubaud
  4. Chi Keung Marco Lau
  5. Mahamitra Das
  6. Libing Fang
  7. Shixuan Wang
  8. Andrew Urquhart

In Information Systems

  1. Elie Bouri
  2. David Roubaud
  3. Rangan Gupta
  4. Chi Keung Marco Lau
  5. Mahamitra Das
  6. Libing Fang
  7. Shixuan Wang
  8. Andrew Urquhart

In Statistical and Nonlinear Physics

  1. Elie Bouri
  2. David Roubaud
  3. Rangan Gupta

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