论文标题
使用V-Transforms和Copulas建模挥发性时间序列
Modelling volatile time series with v-transforms and copulas
论文作者
论文摘要
提出了使用一类具有均匀性的变换的挥发性时间序列建模的方法，以实现均匀的随机变量。 V型转换描述了时间序列的固定分布的分位数与可预测波动率代理变量分布的分位数之间的关系。它们可以表示为Copulas，并允许将任意边缘分布与副氏率工艺相结合的模型的制定和估计。使用高斯ARMA副库过程说明了这个想法，结果模型被证明可以复制许多金融回报系列的程式化事实，并促进计算模型的边际和条件特征，包括风险的量化度量。通过将确切的最大似然方法调整为ARMA过程的估计来进行估计，并且该模型在经验应用中与标准Garch具有竞争力，以对比特币返回数据进行竞争。
An approach to the modelling of volatile time series using a class of uniformity-preserving transforms for uniform random variables is proposed. V-transforms describe the relationship between quantiles of the stationary distribution of the time series and quantiles of the distribution of a predictable volatility proxy variable. They can be represented as copulas and permit the formulation and estimation of models that combine arbitrary marginal distributions with copula processes for the dynamics of the volatility proxy. The idea is illustrated using a Gaussian ARMA copula process and the resulting model is shown to replicate many of the stylized facts of financial return series and to facilitate the calculation of marginal and conditional characteristics of the model including quantile measures of risk. Estimation is carried out by adapting the exact maximum likelihood approach to the estimation of ARMA processes and the model is shown to be competitive with standard GARCH in an empirical application to Bitcoin return data.
