On the time-varying causal relationships that drive bitcoin returns

Thanasis Stengos, Theodore Panagiotidis and Georgios Papapanagiotou

No 2501, Working Papers from University of Guelph, Department of Economics and Finance

Abstract: This paper uses a Bayesian time-varying parameter vector autoregressive (TVP-VAR) model to assess the impact of alternative drivers of bitcoin returns. We consider an extended set of alternative drivers and select the most important variables using a Bayesian variable selection method. To examine the evolution of the Granger-causality relationship between the selected variables and bitcoin returns over time, we employ a new approach based on the estimates of the TVP-VAR model and heteroscedastic-consistent Granger-causality hypothesis testing. In addition, we perform impulse response function and forecast error variance decomposition analysis. The results indicate that investor sentiment and ethereum returns affect bitcoin returns over the entire sample. Trading volume emerges as an important determinant of bitcoin returns when bitcoin prices remain relatively steady.

Keywords: Bayesian VAR; time-varying Granger-causality; bitcoin; cryptocurrency; uncertainty; Google trends (search for similar items in EconPapers)
JEL-codes: C11 C12 C15 C52 D80 E58 G15 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2025
New Economics Papers: this item is included in nep-fdg and nep-pay
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Persistent link: https://EconPapers.repec.org/RePEc:gue:guelph:2025-01

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