论文标题
多元回测和COPULAS进行风险评估
Multivariate backtests and copulas for risk evaluation
论文作者
论文摘要
风险评估是一个预测，其有效性必须进行回测。与点预测相比，这项工作中使用了概率分布预测，并允许进行更强大的验证。我们的目的是使用双变量Copulas来表征样本中的Copulas并验证样本外双变量预测。对于这两个设置，概率积分变换（PIT）和Rosenblatt变换均用于将问题映射到独立的副群中。对于此简单的副物，可以应用统计测试来验证样本中的副物的选择或双变量预测的有效性。显着的结果是，学生副总裁很好地描述了财务时间序列之间的依赖性（无论相关性如何），并且基于历史创新的风险方法学提供的双变量预测可以正确地进行样本。先决条件是要删除异质性，以便具有固定时间序列，在这项工作中，使用了长期内存的拱形波动率模型。
Risk evaluation is a forecast, and its validity must be backtested. Probability distribution forecasts are used in this work and allow for more powerful validations compared to point forecasts. Our aim is to use bivariate copulas in order to characterize the in-sample copulas and to validate out-of-sample a bivariate forecast. For both set-ups, probability integral transforms (PIT) and Rosenblatt transforms are used to map the problem into an independent copula. For this simple copula, statistical tests can be applied to validate the choice of the in-sample copula or the validity of the bivariate forecast. The salient results are that a Student copula describes well the dependencies between financial time series (regardless of the correlation), and that the bivariate forecasts provided by a risk methodology based on historical innovations performs correctly out-of-sample. A prerequisite is to remove the heteroskedasticity in order to have stationary time series, in this work a long-memory ARCH volatility model is used.
