The Efficiency of Value-at-Risk Models during Extreme Market Stress in Cryptocurrencies

Abstract

In recent years, the cryptocurrency market has been experiencing extreme market stress due to unexpected extreme events such as the COVID-19 pandemic, the Russia and Ukraine war, monetary policy uncertainty, and a collapse in the speculative bubble of the cryptocurrencies market. These events cause cryptocurrencies to exhibit higher market risk. As a result, a risk model can lose its accuracy according to the rapid changes in risk levels. Value-at-risk (VaR) is a widely used risk measurement tool that can be applied to various types of assets. In this study, the efficacy of three value-at-risk (VaR) models—namely, Historical Simulation VaR, Delta Normal VaR, and Monte Carlo Simulation VaR—in predicting market stress in the cryptocurrency market was examined. The sample consisted of popular cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), and Ripple (XRP). Backtesting was performed using Kupiec’s POF test, Kupiec’s TUFF test, Independence test, and Christoffersen’s Interval Forecast test. The results indicate that the Historical Simulation VaR model was the most appropriate model for the cryptocurrency market, as it demonstrated the lowest rejections. Conversely, the Delta Normal VaR and Monte Carlo Simulation VaR models consistently overestimated risk at confidence levels of 95% and 90%, respectively. Despite these results, both models were found to exhibit comparable robustness to the Historical Simulation VaR model.

Financial Decisions and Value-at-Risk: Empirical Evidence from BIST 100 Companies

“…In a more recent study, Topaloğlu and Kurt Cihangir (2022) examined the relationship between VaR and stock returns in the Turkish banking market and detected a bidirectional causality relationship between stock returns and Monte-Carlo VaR, while no causality relationship is detected between deltanormal and bootstrap VaRs and stock returns. Likitratcharoen (2023), who used VaR methods to estimate extreme market stress in the cryptocurrency market during the periods Covid-19 pandemic and the Russia-Ukraine war, stated that the historical simulation method is the most appropriate method for VaR calculations in cryptocurrencies. In a similar study, Trucíos and Taylor (2023) stated that the generalized autoregressive score (GAS) model is an appropriate model for VaR and expected loss estimation in the cryptocurrency market.…”

Assessing Financial Stability in Turbulent Times: A Study of Generalized Autoregressive Conditional Heteroskedasticity-Type Value-at-Risk Model Performance in Thailand’s Transportation Sector during COVID-19

“…The applicability of VaR transcends various asset classes. In the realm of high-risk assets like cryptocurrencies, research indicates a superior performance of non-parametric approaches over those predicated on normality assumptions (Likitratcharoen et al 2018(Likitratcharoen et al , 2021(Likitratcharoen et al , 2023. In emerging markets, the deployment of Extreme Value Theory (EVT) for VaR forecasting has garnered attention.…”

“…However, it is not devoid of assumptions. The HS VaR model presupposes that future investment returns will mirror the historical returns dataset, a premise that underlies its quantile estimation (Likitratcharoen et al 2021(Likitratcharoen et al , 2023Pritsker 2006). The model's equation is formulated as:…”