Forecasting Value-at-Risk of cryptocurrencies using the time-varying mixture-accelerating generalized autoregressive score model

【Author】 Jiang, Kunliang; Zeng, Linhui; Song, Jiashan; Liu, Yimeng

【Source】RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE

【影响因子】6.143

【Abstract】We introduce the accelerating generalized autoregressive score (aGAS) technique into the Gaussian-Cauchy mixture model and propose a novel time-varying mixture (TVM)-aGAS model. The TVM-aGAS model is particularly suitable for processing the fat-tailed and extreme volatility characteristics of cryptocurrency returns. We then apply it to Value-at-Risk (VaR) forecasting of three cryptocurrencies, obtaining testing results that show our model possesses advantages in forecasting the density of daily cryptocurrency returns. Compared to other benchmarked models, the proposed model performs well in forecasting out-of-sample VaR. The findings underscore that our method is a useful and reliable alternative for forecasting VaR in cryptocurrencies.

【Keywords】Time-varying mixture model; Accelerating generalized autoregressive score; Cryptocurrency markets; Risk management; Value-at-Risk

【发表时间】2022 OCT

【收录时间】2022-05-21

【文献类型】实证性文章

【主题类别】

区块链治理-市场治理-数字货币