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Modelling the dynamics of Bitcoin and Litecoin: GARCH versus stochastic volatility models

机译:建模比特币和莱特币的动态:GARCH与随机波动率模型

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摘要

We examine and compare a large number of generalized autoregressive conditional heteroskedastic (GARCH) and stochastic volatility (SV) models using series of Bitcoin and Litecoin price returns to assess the model fit for dynamics of these cryptocurrency price returns series. The various models examined include the standard GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, t-distributed and moving average innovations. We report that the best model for Bitcoin is SV-t while it is GARCH-t for Litecoin. Overall, the t-class of models performs better than other classes for both cryptocurrencies. For Bitcoin, the SV models consistently outperform the GARCH models and the same holds true for Litecoin in most cases. Finally, the comparison of GARCH models with GARCH-GJR models reveals that the leverage effect is not significant for cryptocurrencies, suggesting that these do not behave like stock prices.
机译:我们使用比特币和莱特币价格回报率系列来检查和比较大量的广义自回归条件异方差(GARCH)模型和随机波动率(SV)模型,以评估适合这些加密货币价格回报率系列动态的模型。检验的各种模型包括具有AR(1)对数波动过程的标准GARCH(1,1)和SV,以及具有跳跃,均值波动,杠杆效应,t分布和移动平均创新的更灵活的模型。我们报告说,比特币的最佳模型是SV-t,而莱特币的是GARCH-t。总体而言,两种加密货币的t类模型的性能均优于其他类。对于比特币,SV模型始终优于GARCH模型,在大多数情况下,莱特币也是如此。最后,将GARCH模型与GARCH-GJR模型进行比较后发现,杠杆效应对加密货币影响不大,这表明它们的行为不像股票价格。

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