We consider time series representing a wide variety of risk factors in the context of financial risk management. A major issue of these data is the presence of anomalies that induce a miscalibration of the models used to quantify and manage risk, whence potentially erroneous risk measures on their basis. Therefore, the detection of anomalies is of utmost importance in financial risk management. We propose an approach that aims at improving anomaly detection on financial time series, overcoming most of the inherent difficulties. One first concern is to extract from the time series valuable features that ease the anomaly detection task. This step is ensured through a compression and reconstruction of the data with the application of principal component analysis. We define an anomaly score using a feed-forward neural network. A time series is deemed contaminated when its anomaly score exceeds a given cutoff. This cutoff value is not a hand-set parameter, instead it is calibrated as a parameter of the neural network throughout the minimisation of a customized loss function. The efficiency of the proposed model with respect to several well-known anomaly detection algorithms is numerically demonstrated. We show on a practical case of value-at-risk estimation, that the estimation errors are reduced when the proposed anomaly detection model is used, together with a naive imputation approach to correct the anomaly.
翻译:这些数据的一个主要问题是存在异常现象,导致对用于量化和管理风险的模型进行错误校正,从而导致对用于量化和管理风险的模型进行误差,因此,发现异常现象在金融风险管理中至关重要。我们提出一种方法,目的是改进财务时间序列中的异常检测,克服大部分内在困难。第一个关切是从时间序列中提取宝贵的特征,以方便异常检测任务。这一步骤是通过利用主要组成部分分析对数据进行压缩和重组来确保的。我们使用一个饲料前神经网络来定义异常分数。当异常分数超过给定的截断点时,一个时间序列被视为受污染。这一截断值不是人工设定的参数,而是在最小化定制的损失功能的整个过程中作为神经网络参数加以调整。拟议模型在几个众所周知的异常检测算法方面的效率是用数字显示的。我们用一个实际的风险评估案例显示,在拟议的异常点检测模型使用时,将估算误差与天平异常率模型一起降低。