Cryptocurrency Volatility and Tail Risk

An empirical investigation using ARMA-GARCH-X models

Authors

DOI:

https://doi.org/10.20397/2177-6652/2026.v26i1.3386

Keywords:

Cryptocurrencies; Risk Management; ARMA-GARCH; Value-at-Risk; Expected Shortfall.

Abstract

Objective: This study aims to evaluate the performance of different ARMA-GARCH model specifications in the risk management of major cryptocurrencies, investigating whether the inclusion of exogenous variables improves the calibration of risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES).

Methodology: To achieve this objective, 4,032 specifications of the ARMA-GARCH model applied to the ten main cryptocurrencies in trading were tested. The study incorporated the Fear and Greed Index and Bitcoin Trading Volume as exogenous variables in an ARMA-GARCH-X framework, comparing the performance of the different specifications against an ARMA(1,1)-GARCH(1,1) benchmark.

Originality: Despite growing interest in crypto asset risk management, there are still gaps in the literature regarding the effectiveness of incorporating exogenous variables into forecasting models, as well as the increase in the quality of forecasts when using more complex models.

Main results: The results indicate that the inclusion of external variables improves risk calibration in some assets, although the gains are marginal and heterogeneous. There is also no single optimal parameterization, requiring ARMA orders, GARCH specifications, and error distributions to be adjusted for each cryptocurrency.

Theoretical/methodological contributions: From a methodological point of view, the study contributes by demonstrating the importance of specific calibration of ARMA-GARCH models for different cryptocurrencies in risk estimation. Furthermore, the results suggest that, although more complex models can improve tail risk estimation, the gains in predictive power over simpler models are limited.

Keywords: Cryptocurrencies; Risk Management; ARMA-GARCH; Value-at-Risk; Expected Shortfall.

Author Biographies

Octávio Valente Campos, UFMG

PhD in Controllership and Accounting from the Universidade Federal de Minas Gerais (UFMG). PProfessor at the Centro de Pós-graduação e Pesquisas em Controladoria e Contabilidade (CEPCON)

Aureliano Angel Bressan, UFMG

PhD in Applied Economics from the Universidade Federal de Viçosa. Professor in the Department of Administrative Sciences at the Universidade Federal de Minas Gerais

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Published

2026-06-17

How to Cite

Pereira Alves de Abreu, D., Valente Campos, O., & Bressan, A. A. (2026). Cryptocurrency Volatility and Tail Risk: An empirical investigation using ARMA-GARCH-X models. Revista Gestão & Tecnologia, 26(1), 7–40. https://doi.org/10.20397/2177-6652/2026.v26i1.3386

Issue

Vol. 26 No. 1 (2026)

Section

ARTIGO

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