Cross-validation for nonstationary financial time series and hyperparameter selection

Develop cross-validation procedures that appropriately estimate both model parameters and hyperparameters for regularized high-dimensional realized variance forecasting models under nonstationary financial time series, ensuring that temporal ordering and structural changes are respected.

Background

Regularized models such as LASSO require hyperparameter selection, commonly via K-fold cross-validation. In nonstationary financial data, the temporal position of validation folds relative to training folds can affect performance because time direction matters. The authors state that conventional cross-validation may be unsuitable and identify the need for methods tailored to nonstationary settings.