Version 1
: Received: 3 July 2023 / Approved: 3 July 2023 / Online: 4 July 2023 (11:29:36 CEST)
How to cite:
Subramoney, S. D.; Chinhamu, K.; Chifurira, R. VaR Estimation Using Extreme Value Mixture Models for Cryptocurrencies. Preprints2023, 2023070187. https://doi.org/10.20944/preprints202307.0187.v1
Subramoney, S. D.; Chinhamu, K.; Chifurira, R. VaR Estimation Using Extreme Value Mixture Models for Cryptocurrencies. Preprints 2023, 2023070187. https://doi.org/10.20944/preprints202307.0187.v1
Subramoney, S. D.; Chinhamu, K.; Chifurira, R. VaR Estimation Using Extreme Value Mixture Models for Cryptocurrencies. Preprints2023, 2023070187. https://doi.org/10.20944/preprints202307.0187.v1
APA Style
Subramoney, S. D., Chinhamu, K., & Chifurira, R. (2023). VaR Estimation Using Extreme Value Mixture Models for Cryptocurrencies. Preprints. https://doi.org/10.20944/preprints202307.0187.v1
Chicago/Turabian Style
Subramoney, S. D., Knowledge Chinhamu and Retius Chifurira. 2023 "VaR Estimation Using Extreme Value Mixture Models for Cryptocurrencies" Preprints. https://doi.org/10.20944/preprints202307.0187.v1
Abstract
Cryptocurrencies have obtained a crucial position in the international financial landscape. The cryptocurrency market has been perceived as a highly volatile market since the inception of Bitcoin. This study investigates the relevant performance of extreme value models (EVM) in estimating the Value-at-Risk (VaR) of Bitcoin and Ethereum returns. The extreme value mixture models, GPD-Normal-GPD (GNG) and GPD-KDE-GPD models are fitted to the returns of Bitcoin and Ethereum and the Kupiec likelihood backtesting procedure is performed on the VaR estimates to assess the fits. Both models’ results showed that the fits were a much more decent representation of the observed data when compared to the Normal distribution. The backtesting results showed that the GPD-KDE-GPD model’s fit was superior to that of the GPD-Normal-GPD for both sets of returns at all VaR risk levels except at the 99% level. The results of this study may assist with understanding the dynamics and risks associated with cryptocurrencies and can serve as a beneficial tool for decision-making and risk management to investors, traders, financial institutions and many other participants in the cryptocurrency ecosystem.
Keywords
Bitcoin; cryptocurreny; extreme value models (EVM); GPD-Normal-GPD (GNG); generalised Pareto distribution (GPD); Kernel density estimator (KDE); Normal distribution; Value-at-Risk (VaR)
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
