Return, volatility and shock spillovers of Bitcoin with energy and technology companies
Introduction
Cryptocurrencies have received significant attention from investors, media and regulatory authorities with a burgeoning academic interest from computer science to finance literature (Böhme et al., 2015). Despite the huge number of cryptocurrencies, Bitcoin maintains the lion’s share with substantial market capitalization. Bitcoins are backed with blockchain technology which allows a decentralized system for the introduction of new Bitcoins and verification of transactions by solving a crypto-puzzle. The requirements in terms of computing power and energy are enormous as Bitcoin transactions increase, more miners compete in the Bitcoin network, and the crypto-algorithm that verifies blocks and rewards miners becomes more difficult. The total annual energy consumption amounts to 57.69 TWh, close to the electricity needs of Kuwait (BitcoinEnergyConsumption.com, March 2018). Despite the strong interdependence between energy, technology and Bitcoin, their dynamics and economic linkages have not yet been explored.
This study fills the gap by contributing in two ways. First, we use a Vector Autoregression conditional mean process to model returns and the asymmetric BEKK Generalized Autoregressive Conditional Heteroskedasticity process for variances (VAR-BEKK-AGARCH) to examine return, volatility, and shock spillovers between Bitcoin and stock indices of clean energy, fossil fuel energy and technology companies.1 Second, we study portfolio management implications of dynamic conditional correlations in a minimum-variance optimal portfolio.
Our study expands previous efforts in cryptocurrencies’ literature that analyze the diversification benefits and interdependencies with financial assets Dyhrberg (2016), Ciaian et al. (2018), Corbet et al. (2018) and explore Bitcoin returns and volatility Balcilar et al. (2017), Katsiampa (2017). Our work is closely related to the strand that investigates spillovers in energy and technology firms (Sadorsky, 2012) and studies that connect Bitcoin with energy prices, the key element for its production and sustainability Bouri et al. (2017), Hayes (2017).
Our paper is organized as follows. Section 2 presents the data and methodology. Section 3 discusses the empirical findings and Section 4 provides the main conclusions.
Section snippets
Data and methodology
We obtain data for S&P Global Clean Energy Index (SPGCE), MSCI World Energy Index (MSCIWE)2 , MSCI World Information Technology Index (MSCIWIT) and Bitcoin (BTC) from Datastream spanning from August 22, 2011 to February 15, 2018.3 Our sample
Return, volatility and asymmetric shock spillovers
The estimation results are presented in Table 2. We find significant and positive past own return effects on energy indices (, ). Past one-period lagged returns of Bitcoin and technology firms do not help predict short-term returns. The , and parameters in VAR-mean equation reveal unilateral past return spillovers from stock indices to Bitcoin. In other words, higher returns in clean energy companies predict lower returns in Bitcoin, while there is a positive impact of fossil
Conclusions
This study uses a VAR(1)-AGARCH model to analyze spillover effects between Bitcoin and energy and technology companies. Our findings indicate significant return spillovers from energy and technology stocks to Bitcoin. Short-run volatility spills over from technology companies to Bitcoin, while Bitcoin has long-run volatility effects on energy companies. We find bidirectional asymmetric shock spillovers between Bitcoin and stock indices. Finally, we show portfolio management implications and
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