Spillover and Predictability of Volatility of 50 Major Cryptocurrencies: Evidence from a LASSO-Regularized Quantile VAR

Giovanni Bonaccolto (), Sayar Karmakar (), Elie Bouri () and Rangan Gupta
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Giovanni Bonaccolto: Department of Economics and Law, ``Kore" University of Enna, Italy
Sayar Karmakar: Department of Statistics, University of Florida, USA
Elie Bouri: Adnan Kassar School of Business, Lebanese American University, Lebanon

No 202538, Working Papers from University of Pretoria, Department of Economics

Abstract: Previous studies examine spillover effects across the volatility of several cryptocurrencies in the mean or across quantiles without addressing the issue of high dimensionality. Using a large dataset of 50 cryptocurrencies, we employ a LASSO-regularized Quantile VAR framework and show that spillover effects differ across low, medium, and high volatility regimes, especially when evaluated dynamically over time, with sharp increases around tail events such as the war in Ukraine. Importantly, we demonstrate that the LASSO-QVAR model delivers statistically significant forecasting improvements over its univariate counterpart, underscoring the role of interconnectedness in enhancing volatility prediction across cryptocurrencies.

Keywords: Cryptocurrencies, Volatility, LASSO Quantile VAR, Spillovers; Forecasting (search for similar items in EconPapers)
JEL-codes: C32 C53 G10 G17 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2025-09
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