Geopolitical Risk and Cryptocurrency Market Volatility

ABSTRACT

This paper uses a state-dependent local projection model to empirically test the dynamic risk performance of cryptocurrency assets under geopolitical risk events, and to examine whether they have safe-haven properties in the face of major global external shocks. We demonstrate that the volatility of the cryptocurrency market exhibits a non-linear relationship with geopolitical risk. They are uncorrelated in normal times, but the risk of cryptocurrency market rises significantly under extreme geopolitical risk events. The cumulative impulse response pattern of volatility in the cryptocurrency market is similar to that of volatility in speculative assets such as stocks and bonds, but negatively correlated with that of volatility in safe-haven assets such as gold and the U.S. dollar. Our findings suggest that the volatility of cryptocurrencies should not be underestimated when investors consider hedging strategies under external shocks.

KEYWORDS:

• Geopolitical risk
• cryptocurrency market
• state-dependent local projections

JEL:

• F37
• F45
• F65
• G15

Disclosure Statement

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

Notes

1. Cryptocurrency is a form of digital currency that is based on blockchain technology, tracing its origins back to the creation of Bitcoin in 2009. The introduction of Bitcoin marked the start of cryptocurrency, and its decentralized, anonymous, and secure features have attracted a growing number of investors and users. As the price of Bitcoin continues to rise, the cryptocurrency market has expanded. Following Bitcoin, numerous other cryptocurrencies have emerged, including Ethereum, Litecoin, and Ripple. These cryptocurrencies vary in technology and functionality, but together they form a vast cryptocurrency ecosystem.

2. AIC (Akaike Information Criterion) and SBC (Schwarz Bayesian Criterion) are both statistical measures used to compare and select models in statistical modeling and machine learning. AIC is a relative measure of the quality of a statistical model for a given set of data. It is calculated as the difference between the maximum likelihood estimate of the model parameters and the number of parameters in the model. The lower the AIC value, the better the model fits the data. SBC, on the other hand, is a criterion that balances the goodness-of-fit of a model with the complexity of the model. It is calculated as the negative log-likelihood of the data plus a penalty term that increases with the number of parameters in the model. Like AIC, a lower SBC value indicates a better fit for a given set of data. Both AIC and SBC are used in model selection to compare different models and choose the one that best describes the data while avoiding overfitting.

Additional information

Funding

This research was funded by the National Social Science Fund of China [Grant No. 23&ZD058].