Energy Price Inflation, Geopolitical Risk, and Bitcoin Dependence Structure: Evidence from BRICS

ABSTRACT

This study examines the co-movement between geopolitical risk (GPR), energy price, and bitcoin (BTC) in BRICS countries, namely Brazil, Russia, India, China, and South Africa. Previous studies have focused on the impact of GPR on the volatility and risk premium of BTC investment. However, very limited studies have focused on integrating BTC as an extension of the mix of GPR on the co-movement with energy price. The analysis is based on monthly data of GPR index for BRICS countries, brent oil futures, natural gas futures and BTCs covering the period between March 2012 and Jun 2021. We employ the Bayesian graphical structural vector autoregressive model and time-varying parameter vector autoregressions-based dynamic connectedness to investigate the network-dependence structure. This research project provides useful empirical evidence for assessing the impact of both BTC and GPR on energy prices. Nonetheless, it will also be informative about the likelihood of co-movements occurring at different stages.

KEYWORDS:

• Oil prices
• geopolitical risk
• bitcoin
• Bayesian graphical structural vector autoregressive model
• dynamic connectedness

JEL:

• C32
• C58
• G10
• Q03
• Q04

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. GPR index was downloaded from http://www.policyuncertainty.com/gpr.html, the index was contracted by Caldara and Iacoviello (2021). Bitcoin dollar price data sourced from http://coinmetrics.io/data-downloads, and data on energy prices are retrieved from http://fred.stlouisfed.org.

2. Please refer to Ahelegbey, Billio, and Casarin (2016) for estimation and inference technique details. Using the Markov Chain Monte Carlo (MCMC) process and the small-size networks methods, we estimate the LN component and the CN component. The MIN and the MAR structures provide the posterior probabilities for the instantaneous and the lagged relationships between the logarithmic returns of response variables and predictor variables, respectively.

3. Please refer to Antonakakis, Chatziantoniou, and Gabauer (2020) for full details in deriving the TVP-VAR connectedness.