spb/wp12_uqo

1\begin{tabular}{lcccccccc}2\toprule3 & \multicolumn{4}{c}{$h = 1$} & \multicolumn{4}{c}{$h = 22$} \\4\cmidrule(lr){2-5}\cmidrule(lr){6-9}5Model & Equities & FX & Crypto & Futures & Equities & FX & Crypto & Futures \\6\midrule7\multicolumn{9}{l}{\itshape A. GARCH family}\\8\quad GARCH & 1.067 & 1.238 & 1.597 & 1.170 & 0.920 & 1.169 & 1.641 & 0.914 \\9\quad GJR & 1.049 & 1.322 & 1.598 & 1.152 & 0.894 & 1.269 & 1.642 & 0.904 \\10\quad EGARCH & 1.442 & 1.808 & 1.650 & 1.394 & 0.979 & 1.345 & 1.614 & 1.020 \\11\quad RealGARCH & 0.766 & 0.925 & 0.709 & 0.907 & 0.883 & 0.912 & 0.832 & 0.936 \\12\multicolumn{9}{l}{\itshape B. HAR family}\\13\quad HAR-J & 1.030 & 1.070 & 0.816 & 0.993 & 1.001 & 1.000 & 1.029 & 0.999 \\14\quad HAR-CJ & 1.038 & 0.978 & 0.818 & 1.003 & 1.023 & 0.976 & 1.153 & 0.995 \\15\quad SHAR & 1.013 & 1.027 & 0.951 & 1.001 & 1.010 & 0.987 & 0.998 & 1.006 \\16\quad HARQ & 1.755 & 1.613 & 1.916 & 3.286 & 1.586 & 0.995 & 1.608 & 2.242 \\17\quad LogHAR & 0.763 & 0.912 & 0.652 & 0.879 & 0.837 & 0.911 & 0.670 & 0.847 \\18\multicolumn{9}{l}{\itshape C. Machine learning --- HAR information set}\\19\quad Ridge-H & 0.761 & 0.914 & 0.638 & 0.879 & 0.842 & 0.912 & 0.685 & 0.851 \\20\quad LASSO-H & 0.766 & 0.920 & 0.639 & 0.903 & 0.856 & 0.923 & 0.698 & 0.905 \\21\quad ElasticNet-H & 0.762 & 0.915 & 0.638 & 0.882 & 0.844 & 0.912 & 0.686 & 0.856 \\22\quad RF-H & 0.930 & 0.968 & 0.678 & 1.071 & 0.992 & 0.979 & 0.774 & 1.199 \\23\quad XGBoost-H & 0.948 & 1.055 & 0.748 & 1.126 & 1.150 & 1.141 & 0.883 & 1.376 \\24\quad LightGBM-H & 1.013 & 1.111 & 0.739 & 1.168 & 1.154 & 1.183 & 0.889 & 1.362 \\25\multicolumn{9}{l}{\itshape D. Machine learning --- extended information set}\\26\quad Ridge-X & 0.715 & \textbf{0.852} & \textbf{0.621} & \textbf{0.778} & 0.856 & 0.977 & 0.647 & \textbf{0.823} \\27\quad LASSO-X & 0.723 & 0.860 & 0.629 & 0.783 & 0.849 & 0.970 & 0.670 & 0.854 \\28\quad ElasticNet-X & \textbf{0.715} & 0.856 & 0.623 & 0.780 & 0.849 & 0.963 & 0.647 & 0.826 \\29\quad RF-X & 0.839 & 0.921 & 0.624 & 1.014 & 0.993 & 0.954 & 0.749 & 1.188 \\30\quad XGBoost-X & 0.877 & 0.972 & 0.630 & 0.965 & 1.201 & 1.135 & 0.892 & 1.347 \\31\quad LightGBM-X & 0.947 & 1.019 & 0.668 & 1.028 & 1.225 & 1.185 & 0.937 & 1.377 \\32\multicolumn{9}{l}{\itshape E. Deep learning and pooled}\\33\quad LSTM & 1.451 & 2.006 & 0.883 & 1.752 & 1.088 & 1.557 & 0.822 & 1.148 \\34\quad Transformer & 2.022 & 1.783 & 1.403 & 3.099 & 1.469 & 1.769 & 1.086 & 1.920 \\35\quad Pooled-LGBM & 0.794 & 0.943 & 0.648 & 0.883 & 1.071 & 1.033 & 0.773 & 1.007 \\36\multicolumn{9}{l}{\itshape F. Forecast combinations}\\37\quad Comb-Mean & 0.770 & 0.974 & 0.740 & 0.864 & 0.862 & 0.940 & 0.836 & 0.894 \\38\quad Comb-InvMSE & 0.740 & 0.889 & 0.625 & 0.824 & \textbf{0.827} & \textbf{0.880} & \textbf{0.646} & 0.843 \\39\bottomrule40\end{tabular}41