spb/wp12_uqo

1\begin{tabular}{lcccc}2\toprule3Model & 2013--19 & 2020 & 2021--22 & 2024--26 \\4\midrule5\multicolumn{5}{l}{\itshape A. GARCH family}\\6\quad GARCH & 1.468 & 1.126 & 1.678 & 1.169 \\7\quad GJR & 1.447 & 1.235 & 1.683 & 1.140 \\8\quad EGARCH & 1.733 & 1.464 & 1.679 & 1.547 \\9\quad RealGARCH & 0.546 & 0.506 & 0.937 & 0.961 \\10\multicolumn{5}{l}{\itshape B. HAR family}\\11\quad HAR & 1.000 & 1.000 & 1.000 & 1.000 \\12\quad HAR-J & 0.648 & 1.001 & 1.059 & 1.026 \\13\quad HAR-CJ & 0.638 & 1.021 & 1.032 & 1.015 \\14\quad SHAR & 0.645 & 1.214 & 1.173 & 1.007 \\15\quad HARQ & 2.630 & 3.326 & 1.479 & 1.670 \\16\quad LogHAR & 0.537 & 0.500 & 0.837 & 0.951 \\17\multicolumn{5}{l}{\itshape C. Machine learning --- HAR information set}\\18\quad Ridge-H & 0.541 & 0.497 & 0.817 & 0.945 \\19\quad LASSO-H & 0.544 & 0.512 & 0.823 & 0.950 \\20\quad ElasticNet-H & 0.541 & 0.498 & 0.818 & 0.946 \\21\quad RF-H & 0.613 & 0.888 & 0.840 & 1.023 \\22\quad XGBoost-H & 0.619 & 0.848 & 0.989 & 1.090 \\23\quad LightGBM-H & 0.665 & 0.879 & 1.000 & 1.111 \\24\multicolumn{5}{l}{\itshape D. Machine learning --- extended information set}\\25\quad Ridge-X & \textbf{0.510} & \textbf{0.467} & \textbf{0.775} & \textbf{0.883} \\26\quad LASSO-X & 0.517 & 0.469 & 0.785 & 0.892 \\27\quad ElasticNet-X & 0.511 & 0.467 & 0.777 & 0.885 \\28\quad RF-X & 0.550 & 0.802 & 0.804 & 0.936 \\29\quad XGBoost-X & 0.592 & 0.723 & 0.860 & 0.955 \\30\quad LightGBM-X & 0.647 & 0.788 & 0.928 & 1.003 \\31\multicolumn{5}{l}{\itshape E. Deep learning and pooled}\\32\quad LSTM & 1.228 & 1.286 & 1.373 & 1.392 \\33\quad Transformer & 1.833 & 3.202 & 1.718 & 1.567 \\34\quad Pooled-LGBM & 0.606 & 0.483 & 0.867 & 0.933 \\35\multicolumn{5}{l}{\itshape F. Forecast combinations}\\36\quad Comb-Mean & 0.607 & 0.608 & 0.899 & 0.944 \\37\quad Comb-InvMSE & 0.538 & 0.487 & 0.799 & 0.902 \\38\bottomrule39\end{tabular}40