Dynamic Connectedness between Green Energy and Fossil Energy Markets


Evrim Mandacı P., Çağlı E. Ç., Taşkın F. D., Tedik Kocakaya B.

7th International Conference on Energy Economics and Energy Policy (ICEEEP 2023), Barcelona, Spain, 28 - 30 April 2023, pp.1-8

  • Publication Type: Conference Paper / Full Text
  • City: Barcelona
  • Country: Spain
  • Page Numbers: pp.1-8
  • Dokuz Eylül University Affiliated: Yes

Abstract

This paper determines the existence of “herd behavior” and the factors of herding in cryptocurrency markets. We examine the causability between herding and sentiment signals such as (FoMO, hopeful, negative, positive, and uncertain) as well as between herding and volatility and valume as a measure of overconfidence for Bitcoin. Our dataset covers January 1, 2019 – September 10, 2022, consisting of COVID 19 pandemic. First, we use intraday aggregate trade data and construct daily herding intensity statistics for negative, positive, and zero trades in the sense of Patterson and Sharma (2006). Then, we compute realized volatility series exploiting the Parkinson’s (1980) range-based measure. Following Balcilar et al. (2017), the volatility series are log-detrended. The sentiment signals are smoothed using the exponential smoothing (ETS) models in the statsmodule Python module. Lastly, we estimate Fourier-type Granger causality tests developed by Nazlioglu et al. (2019). Our results show the bi-directional causality between sentiment signals from Twitter and the herding intensity statistics at the conventional significance levels. The sentiment signals from Reddit have a limited impact on the herding statistics, but most of the signals significantly cause the volatility measures. Bitcointalk sentiment signals do not cause any herding, volatility, and volume measures. Our results provide important implications for investors and portfolio managers interested in cryptocurrency investments