Assessing the Newly Carbon-Backed Cryptocurrency Downside Risk: Insights from Value at Risk and Expected Shortfall Estimations
Received: 14 March 2025 | Revised: 2 April 2025 and 12 April 2025 | Accepted: 15 April 2025 | Online: 19 July 2025
Corresponding author: Chaker Aloui
Abstract
This study presents a method for predicting volatility and assessing the risk and expected shortfall for carbon-backed cryptocurrencies, investigating whether accounting for long memory in cryptocurrency volatilities, asymmetry, and fat-tailed returns improves forecasting and risk quantification. Various long-memory GARCH-class models were investigated under normal and skewed Student-t distributions and the one-ahead value at risk and expected shortfall. The results show that FIEGARCH under skewed Student t-distribution is a strong fit for carbon credit cryptocurrency volatilities, outperforming other FIGARCH models under normal and skewed Student t-distributions. The model consistently produced accurate values of risk and expected shortfalls for both short- and long-term trading positions in and out of the sample. This research provides unique insights into carbon credit cryptocurrencies and offers practical implications for operational risk management for portfolio managers, cryptocurrency traders, and eco-friendly cryptocurrency market regulators, enhancing their ability to manage risk in the evolving cryptocurrency market.
Keywords:
carbon-backed cryptocurrency, volatility, value at risk, expected shortfal, forecastingDownloads
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