A wavelet approach of investing behaviors and their effects on risk exposures

Financial Innovation

Table 9 CAPM-EGARCH Estimates

CAPM-EGARCH	\(\beta_{m}\)	T-stat	R2	LB	ARCH	JB
\(\beta_{m}\) < 1
Essilor	0.525	33.527	0.31	2.02	3.59	16,301
Sodexo	0.559	38.830	0.35	0.41	0.72	9359
Ricard	0.627	35.094	0.355	5.79	0.386	9427
Danone	0.697	49.456	0.41	4.11	2.26	4732
Publicis	0.7	43.134	0.43	6	0.76	2019
Orange	0.722	51.499	0.43	2.27	0.46	4566
L’Oréal	0.755	55.404	0.486	1.34	2.04	4675
Vivendi	0.788	57.146	0.52	1.51	0.16	7736
Veolia	0.838	41.000	0.39	0.24	1.35	152,130
Air Liquide	0.851	65.115	0.65	2.9	0.21	7518
Carrefour	0.866	49.282	0.48	1.32	1.14	3813
Total	0.887	73.458	0.67	2.47	0.68	2273
GDF	0.942	62.613	0.49	11.6	0.33	148,170
Airbus	0.945	42.384	0.35	3.94	0.17	105,930
Accor	0.954	41.580	0.48	1.4	1.07	5617
\(\beta_{m}\) = 1
Technip	0.991	35.798	0.39	3.22	1.22	6331
Bouygues	0.994	51.512	0.5	0.24	0.84	17,744
Gemini	0.996	46.108	0.484	2.27	0.35	2782
Michelin	1.017	44.993	0.49	4.46	2.41	3560
\(\beta_{m}\) > 1
LVMH	1.028	72.783	0.62	1.79	0.31	10,806
Vinci	1.062	72.543	0.67	1.12	0.875	5150
Alcatel	1.131	37.546	0.32	0.846	1.56	14,264
PSA	1.135	46.521	0.39	3.63	0.07	1539
Schneider	1.192	70.704	0.68	9.93	2.533	1063
St-Gobain	1.25	64.465	0.67	2.04	0.23	15,825
Renault	1.268	54.896	0.55	1.16	2.05	2262
AXA	1.288	70.891	0.67	4.38	0.013	43,151
BNP	1.289	77.316	0.61	2.94	0.96	40,217
SG	1.305	62.943	0.56	5.07	4.53	10,525
CA	1.351	37.302	0.56	2.62	1.58	7842

At 5% risk level, Column LB (Ljung–Box test): χ²(5) = 11.1; Column ARCH (ARCH-LM test): χ²(5) = 11.1 Column J–B (Jarque–Bera Line): χ²(2) = 5.99. We use Weighted Tests of Ljung–Box and ARCH-M of Fisher and Gallagher (2012)