How is price explosivity triggered in the cryptocurrency markets?

Yuzhi Cai,Thanaset Chevapatrakul,Danilo V Mascia

doi:10.1007/s10479-021-04298-4

Abstract

We shed light on how the price explosivity characterising Bitcoin and other major cryptocurrencies is triggered, by employing the Quantile Self-Exciting Threshold Autoregressive (QSETAR) model. Our results for Bitcoin, Ripple, and Stellar reveal that the explosive behaviour originates from the extreme upper tails of the return distributions following a price increase in the preceding day. We do not find evidence of explositivity in the price of Litecoin.

Highlights

Much has been written about Bitcoin—and other cryptocurrencies—since its inception in 2008
We characterise the bubble-like behaviour of prices of four major cryptocurrencies using the Quantile Self-Exciting Threshold Autoregressive (QSETAR) model of Cai and Stander (2008)
The results show that the QSETAR models fit the data very well and that they outperform the conventional SETAR model

Summary

Introduction

Much has been written about Bitcoin—and other cryptocurrencies—since its inception in 2008. The “bubble-like” and “explosive” behaviour of BTC/USD, clearly illustrated, might be ascribed to the relative newness of the cryptocurrency markets and the consequent appeal that this generates on speculators (Bouri et al 2019). Cagli (2019) widens his sample and includes some more altcoins in order to detect potential price explosivity and pairwise comovements in their explosive behaviour He finds all cryptocurrencies, except one, to show explosivity, as well as a number of co-explosive relationships in some altcoins pairs. In this article, using the Quantile Self-Exciting Threshold Autoregressive (QSETAR) model, we characterise the bubble-like behaviour in the prices of Bitcoin and three other major cryptocurrencies: Litecoin, Ripple, and Stellar.. Our empirical results for Bitcoin, Ripple, and Stellar show that the explosive behaviour originates only from the extreme right tail of the return distribution following a price increase in the preceding day.

Methodology

Empirical analysis and results

Findings

Concluding remarks