Abstract: A regularization-based (RB) hedging tool identifies a recommended hedging portfolio that track a target asset and provides one or metrics indicating a predicted performance of the hedging portfolio relative to the target asset. The RB hedging tool uses a RB hedging model that is trained on price data from an observation period. Initial hyperparameters for the model are selected using asset price data from a validation period and the performance of the model is evaluated by applying it to asset price data from a backtest period. The end-user is presented with one or more metrics indicating the performance of the model and may modify one or more settings (e.g., hyperparameters) of the model. The model is retrained and reapplied to the backtest period, and the metrics are updated. Thus, end-users may tailor the model to their own particular needs and preferences.

Abstract: A regularization-based (RB) hedging tool identifies a recommended hedging portfolio that track a target asset and provides one or metrics indicating a predicted performance of the hedging portfolio relative to the target asset. The RB hedging tool uses a RB hedging model that is trained on price data from an observation period. Initial hyperparameters for the model are selected using asset price data from a validation period and the performance of the model is evaluated by applying it to asset price data from a backtest period. The end-user is presented with one or more metrics indicating the performance of the model and may modify one or more settings (e.g., hyperparameters) of the model. The model is retrained and reapplied to the backtest period, and the metrics are updated. Thus, end-users may tailor the model to their own particular needs and preferences.

Abstract: A regularization-based (RB) hedging tool identifies a recommended hedging portfolio that track a target asset and provides one or metrics indicating a predicted performance of the hedging portfolio relative to the target asset. The RB hedging tool uses a RB hedging model that is trained on price data from an observation period. Initial hyperparameters for the model are selected using asset price data from a validation period and the performance of the model is evaluated by applying it to asset price data from a backtest period. The end-user is presented with one or more metrics indicating the performance of the model and may modify one or more settings (e.g., hyperparameters) of the model. The model is retrained and reapplied to the backtest period, and the metrics are updated. Thus, end-users may tailor the model to their own particular needs and preferences.