Research Article
Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns
Table 2
Baseline volatility models.
| Baseline GARCH models, single regime |
| (1) RW | C | | | | | | | |
| | 0.001873*** (0.000391) | | | | | | | 12241.56 |
| (2) GARCH | ARCH | GARCH | C | | | | | |
| |
0.1591259*** (0.0068625) | 0.8280324*** (0.0056163) | 0.0000206*** () | | | | | 13174.66 |
| (3) APGARCH | APARCH | APARCH_E | PGARCH | POWER | C | | | |
| | 0.2153934*** (0.0076925) | −0.0319876*** (0.0160775) | 0.7801279*** (0.0074049) | 1.354999*** (0.0707303) | 0.0004055*** (0.0001074) | | | 13111.96 |
| (4) FIAPGARCH | ARCH (Phi1) | GARCH (Beta1) | D-FIGARCH | APARCH (Gamma1) | APARCH (Delta) | C (MEAN) | C (VAR.) | |
| | 0.250877*** (0.096563) | 0.449631*** (0.10367) | 0.417296*** (0.040388) | 0.027382 (0.033565) | 2.020946*** (0.096663) | 0.001635*** (0.00028929) | 8.054283 (6.6685) | 13196.30 |
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