A major concern when dealing with financial time series involving a wide variety ofmarket risk factors is the presence of anomalies. These induce a miscalibration of the models used toquantify and manage risk, resulting in potential erroneous risk measures. We propose an approachthat aims to improve anomaly detection in financial time series, overcoming most of the inherentdifficulties. Valuable features are extracted from the time series by compressing and reconstructingthe data through principal component analysis. We then define an anomaly score using a feedforwardneural network. A time series is considered to be contaminated when its anomaly score exceeds agiven cutoff value. This cutoff value is not a hand-set parameter but rather is calibrated as a neuralnetwork parameter throughout the minimization of a customized loss function. The efficiency of theproposed approach compared to several well-known anomaly detection algorithms is numericallydemonstrated on both synthetic and real data sets, with high and stable performance being achievedwith the PCA NN approach. We show that value-at-risk estimation errors are reduced when theproposed anomaly detection model is used with a basic imputation approach to correct the anomaly.
翻译:处理涉及多种市场风险因素的金融时间序列时,一个主要关切是存在异常现象。这些异常现象导致对用于量化和管理风险的模型进行错误校正,从而产生潜在的错误风险措施。我们提出一种旨在改进财务时间序列中异常现象探测的方法,克服了大部分固有的困难。通过主要组成部分分析压缩和重整数据,从时间序列中提取了有价值的特征。然后我们用一个饲料前向网络来定义异常分数。当异常分数超过给定截断值时,一个时间序列被认为是被污染的。这一截断值不是一个手设参数,而是在尽可能减少定制损失函数的整个过程中被校准为神经网络参数。与一些广为人知的异常检测算法相比,拟议方法在合成和真实数据集中的效率得到了数字化的验证,在使用计算机辅助系统NNP方法时,正在实现高和稳定的性能。我们表明,当拟议异常点检测模型使用基本内插法来纠正异常现象时,风险估计误差会减少。