Subramoney et al. / International Journal of Finance & Banking Studies, Vol 10 No 4, 2021 ISSN: 2147-4486 Peer-reviewed Academic Journal published by SSBFNET with respect to copyright holders. Page40 Finance & Banking Studies IJFBS, VOL 10 NO 4 ISSN: 2147-4486 Contents available at www.ssbfnet.com/ojs https://doi.org/10.20525/ijfbs.v10i4.1316 Value at Risk estimation using GAS models with heavy tailed distributions for cryptocurrencies * Stephanie Danielle Subramoney School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa, ORCID ID: 0000-0003-3615-9585 Knowledge Chinhamu School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa, ORCID ID: 0000-0003-4296-7677 Retius Chifurira School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa, ORCID ID: 0000-0001-7889-3417 Abstract Risk management and prediction of market losses of cryptocurrencies are of notable value to risk managers, portfolio managers, financial market researchers and academics. One of the most common measures of an asset’s risk is Value-at-Risk (VaR). This paper evaluates and compares the performance of generalized autoregressive score (GAS) combined with heavy-tailed distributions, in estimating the VaR of two well-known cryptocurrencies’ returns, namely Bitcoin returns and Ethereum returns. In this paper, we proposed a VaR model for Bitcoin and Ethereum returns, namely the GAS model combined with the generalized lambda distribution (GLD), referred to as the GAS-GLD model. The relative performance of the GAS-GLD models was compared to the models proposed by Troster et al. (2018), in other words, GAS models combined with asymmetric Laplace distribution (ALD), the asymmetric Student’s t-distribution (AST) and the skew Student’s t-distribution (SSTD). The Kupiec likelihood ratio test was used to assess the adequacy of the proposed models. The principal findings suggest that the GAS models with heavy-tailed innovation distributions are, in fact, appropriate for modelling cryptocurrency returns, with the GAS-GLD being the most adequate for the Bitcoin returns at various VaR levels, and both GAS-SSTD, GAS-ALD and GAS-GLD models being the most appropriate for the Ethereum returns at the VaR levels used in this study. Keywords: Bitcoin; Cryptocurrency; Ethereum; Generalized lambda distribution (GLD); GAS; Value-at-Risk JEL Classifications: C2; C58; G32 * This paper is derived from the MSc dissertation entitled “A comparison of GARCH-type models and GAS models for cryptocurrencies at University of KwaZulu-Natal”.