Systems and methods for value at risk anomaly detection using a hybrid of deep learning and time series models

A system for Value at Risk (VaR) anomaly detection, comprising:

• a VaR calculation engine comprising at least one computer processor, the VaR calculation engine configured to calculate a current period VaR;
• a VaR anomaly detection engine comprising at least one computer processor, the VaR anomaly detection engine configured to calculate a forecasted VaR and detect an anomaly;
• wherein:
• the VaR calculation engine calculates the current period VaR;
• the VaR anomaly detection engine trains at least one hybrid model using historical VaR calculations;
• the VaR anomaly detection engine calculates the forecasted VaR value using the at least one hybrid model;
• the VaR anomaly detection engine detects an anomaly based on the current period VaR and the forecasted VaR;
• and
• the VaR anomaly detection engine generates a notification in response to the detected anomaly.