Dynamic price and volatility relationships between crypto returns and twitter- based economic uncertainty

In this study we examined the mean and the volatility transmissions of Twitter-based uncertainty and returns of Bitcoin, Ethereum, BNB, XRP, and Cardano in a time- varying context and considered the structural changes. The availability of the data directs us to have a different starting date for the analysis of five cryptocurrencies. However, our data span for all types of cryptocurrencies ended on August 31, 2022. We made several findings. First, our findings indicate that Twitter-based uncertainty has a more robust predictive power on cryptocurrency returns in the time-varying context compared with the standard Fourier-Granger causality results. Second, the empirical findings of the volatility transmission analysis also provide similar results. Third, the findings of the dynamic Fourier variance causality analysis exhibit robust evidence for the bidirectional volatility spillovers between the uncertainty indicator and the cryptocurrency returns, which in turn suggests a significant risk transmission.

Authors

Institutions

Publication Details

Journal
Panoeconomicus
Published
2025-01-01
DOI
https://doi.org/10.2298/pan221120020y
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dynamic price and volatility relationships between crypto returns and twitter- based economic uncertainty

Veli Yılancı, Önder Özgür
Panoeconomicus
Market Dynamics and Volatility
article

Dynamic price and volatility relationships between crypto returns and twitter- based economic uncertainty

Veli Yılancı, Önder Özgür
article en

Abstract

In this study we examined the mean and the volatility transmissions of Twitter-based uncertainty and returns of Bitcoin, Ethereum, BNB, XRP, and Cardano in a time- varying context and considered the structural changes. The availability of the data directs us to have a different starting date for the analysis of five cryptocurrencies. However, our data span for all types of cryptocurrencies ended on August 31, 2022. We made several findings. First, our findings indicate that Twitter-based uncertainty has a more robust predictive power on cryptocurrency returns in the time-varying context compared with the standard Fourier-Granger causality results. Second, the empirical findings of the volatility transmission analysis also provide similar results. Third, the findings of the dynamic Fourier variance causality analysis exhibit robust evidence for the bidirectional volatility spillovers between the uncertainty indicator and the cryptocurrency returns, which in turn suggests a significant risk transmission.

Panoeconomicus(00)
Çanakkale Onsekiz Mart Üniversitesi (TR), Ankara Yıldırım Beyazıt University (TR)
Openalex Percentile: Top 81%
Market Dynamics and Volatility
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.