Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

Table 7

Error statistics of each method for all maturities for test dataset 2 with the subperiod 1 training set (case 2).

Type	Method	6M	1Y	2Y	3Y	4Y	5Y	7Y	10Y	20Y	30Y	Average
RMSE	N-S	3.75	4.10	4.11	4.00	4.29	5.27	5.29	4.95	4.03	3.71	4.35
	RNN	2.78	4.25	3.71	3.48	3.99	4.92	5.63	5.18	3.69	3.20	4.08
	LSTM	3.89	4.20	3.71	3.50	3.57	4.56	5.64	5.19	3.68	3.36	4.13
	SVR	4.04	4.39	3.77	3.46	4.02	5.10	5.85	5.37	3.63	3.30	4.29
	GMDH	3.92	4.18	3.63	3.30	3.78	4.70	5.44	5.00	3.62	3.36	4.09
MSE	N-S	14.03	16.80	16.90	16.00	18.36	27.82	27.99	24.51	16.25	13.75	19.24
	RNN	7.74	18.07	13.78	12.13	15.90	24.20	31.69	26.86	13.59	10.27	17.42
	LSTM	15.10	17.65	13.74	12.24	12.74	20.82	31.77	26.93	13.53	11.27	17.58
	SVR	16.33	19.30	14.22	11.95	16.16	25.98	34.26	28.86	13.18	10.87	19.11
	GMDH	15.36	17.43	13.19	10.88	14.30	22.11	29.62	25.03	13.07	11.32	17.23
MAPE (%)	N-S	28.13	20.83	13.93	11.42	12.08	13.47	11.50	9.20	6.08	6.83	13.35
	RNN	19.36	28.14	16.36	11.69	11.16	11.68	11.62	9.71	6.26	5.19	13.11
	LSTM	26.75	26.79	16.36	11.79	10.41	10.59	11.61	9.82	6.32	5.51	13.60
	SVR	27.32	28.52	16.40	11.48	11.14	12.07	12.27	10.11	6.19	5.36	14.09
	GMDH	26.44	26.58	15.65	10.83	10.48	11.16	11.48	9.44	6.16	5.46	13.37
MPE (%)	N-S	−14.07	6.80	6.03	0.59	−3.99	−7.69	−3.05	−0.41	3.37	−4.72	−1.72
	RNN	−13.08	−8.39	−4.84	−3.65	−3.37	−2.35	−1.26	−1.28	−0.74	0.27	−3.87
	LSTM	−6.75	−4.96	−5.07	−3.58	−4.29	−1.35	−1.20	−1.85	−1.26	−0.71	−3.10
	SVR	−7.37	−8.40	−4.46	−3.08	−2.58	−2.49	−2.40	−1.82	−0.71	−0.49	−3.38
	GMDH	−5.89	−6.42	−4.03	−2.44	−2.42	−2.49	−2.38	−1.75	−0.60	−0.36	−2.88
MAE	N-S	3.18	3.30	3.07	3.04	3.62	4.46	4.52	4.16	3.09	3.15	3.56
	RNN	2.28	3.58	3.02	2.84	3.34	4.13	4.68	4.34	2.99	2.54	3.37
	LSTM	3.33	3.57	3.01	2.86	3.11	3.76	4.68	4.36	3.00	2.66	3.43
	SVR	3.41	3.67	3.05	2.81	3.39	4.27	4.88	4.50	2.96	2.60	3.55
	GMDH	3.32	3.47	2.93	2.67	3.18	3.92	4.54	4.19	2.95	2.65	3.38