Robust estimation of the range-based GARCH model: Forecasting volatility, value at risk and expected shortfall of cryptocurrencies

【Author】 Fiszeder, Piotr; Malecka, Marta; Molnar, Peter

【Source】ECONOMIC MODELLING

【影响因子】3.875

【Abstract】Traditional volatility models do not work well when volatility changes rapidly and in the presence of outliers. Therefore, two lines of improvements have been developed separately in the existing literature. Range-based models benefit from efficient volatility estimates based on low and high prices, while robust methods deal with outliers. We propose a range-based GARCH model with a bounded M-estimator, which combines these two improvements with a third new improvement: a modified robust method, which adds elasticity in treating the outliers. We apply this model to Bitcoin, Ethereum Classic, Ethereum, and Litecoin and find that it forecasts variances, value at risk, and expected shortfall more accurately than the standard GARCH model, the standard range-based GARCH model, and the GARCH model with the robust estimation. Utilization of high and low prices joined with a novel treatment of outliers makes our model perform well during extreme periods when traditional volatility models fail.

【Keywords】Cryptocurrency; Bitcoin; Volatility models; Value at risk; Expected shortfall; High-low range; Robust estimation; Outliers

【发表时间】2024 DEC

【收录时间】2024-10-18

【文献类型】理论模型

【主题类别】

区块链治理-技术治理-交易预测