Carbon emission trading and equity markets in China: How liquidity is impacting carbon returns?

Figures & data

Table 1. Descriptive statistics for market variables.

	Mean	Min	Max	Std.dev.	Skewness	Kurtosis	ADF
MktRV	15.121	3.610	102.216	10.883	3.220	14.896	−3.482***
MktRet	0.000	−0.085	0.058	0.014	−0.893	6.490	−7.938***
MktVol	26.079	24.601	27.901	0.596	0.235	0.215	−2.925***
MktIlliq	−31.375	−37.978	−27.758	1.228	−0.852	1.466	−4.633***
AGGCRet	0.001	−0.512	0.717	0.054	1.632	32.631	−7.794***
AGGCVol	14.313	4.603	18.518	1.699	−1.426	4.345	−3.993***
AGGCIlliq	−16.234	−23.614	0.000	4.343	1.945	4.124	−6.972***

Notes: This table reports the descriptive statistics of daily variables, where ADF refers to the statistics of stationary tests. The statistical significance at 1%, 5%, and 10% levels are represented as ***, ** and *, respectively.

Sources: Authors.

Table 2. Estimation results of Fama-MacBeth regressions.

Variables	$X=\mathit{Vol}$	$X=\mathit{Illiq}$
Intercept	0.017***	0.017***
t-stat	(2.807)	(2.807)
$X_{i,t-1}$	0.029	−0.025
t-stat	(1.254)	(-1.416)
$X_{i,t-2}$	−0.026*	0.029**
t-stat	(-1.667)	(2.118)
$X_{i,t-3}$	0.006	−0.001
t-stat	(0.957)	(-0.250)
$X_{i,t-4}$	−0.013	0.017
t-stat	(-1.123)	(1.478)
$X_{i,t-5}$	−0.013	0.002
t-stat	(-1.600)	(0.660)

Notes: This table shows the results of the Fama-MacBeth regression estimates. The dependent variable is daily returns of individual carbon market. The sample has daily observations from 2013-2021. The statistical significance at 1%, 5%, and 10% levels are represented as ***, ** and *, respectively.

Sources: Authors.

Table 3. Equation results of VAR(2).

Variables	Equations
	${\mathit{MktRV}}_t$	${\mathit{MktRet}}_t$	${\mathit{MktVol}}_t$	${\mathit{MktIlliq}}_t$	${\mathit{AGGCRet}}_t$	${\mathit{AGGCVol}}_t$	${\mathit{AGGCIlliq}}_t$
Intercept	−42.509***	0.425%	0.579***	−6.441***	6.746%	−2.493*	15.824***
t-stat	(-5.550)	(0.236)	(2.854)	(-4.489)	(0.982)	(-1.810)	(3.103)
${\mathit{MktRV}}_{t-1}$	0.425***	0.013%**	−0.000	0.025***	−0.014%	−0.012**	0.023***
t-stat	(16.314)	(2.044)	(-0.570)	(5.088)	(-0.619)	(-2.536)	(1.326)
${\mathit{MktRet}}_{t-1}$	−108.297***	7.684%***	3.441***	−2.627	6.795%	−2.214	7.956
t-stat	(-9.583)	(2.887)	(11.490)	(-1.241)	(0.671)	(-1.090)	(1.058)
${\mathit{MktVol}}_{t-1}$	3.052***	−0.326%	0.644***	−0.824***	−0.120%	0.391**	0.786
t-stat	(3.017)	(-1.367)	(24.035)	(-4.346)	(-0.132)	(2.150)	(1.167)
${\mathit{MktIlliq}}_{t-1}$	0.527***	0.080%**	0.006*	−0.015	0.176%	−0.048**	−0.001
t-stat	(4.013)	(2.584)	(1.786)	(-0.610)	(1.495)	(-2.045)	(-0.014)
${\mathit{AGGCRet}}_{t-1}$	1.572	−0.224%	0.075	0.257	−16.364%***	−0.031	1.490
t-stat	(0.601)	(-0.363)	(1.078)	(0.526)	(-6.981)	(-0.066)	(0.856)
${\mathit{AGGCVol}}_{t-1}$	−0.047	−0.053%*	−0.007**	0.026	0.001%	0.478***	−0.398***
t-stat	(-0.366)	(-1.761)	(-2.065)	(1.068)	(0.006)	(20.801)	(-4.680)
${\mathit{AGGCIlliq}}_{t-1}$	0.009	0.000%	−0.000	−0.000	−0.026%	0.000	0.166***
t-stat	(0.253)	(0.000)	(-0.473)	(-0.014)	(-0.804)	(0.006)	(6.858)
${\mathit{MktRV}}_{t-2}$	0.348***	−0.019%***	−0.001	0.019***	0.012%	0.008*	0.002
t-stat	(14.012)	(-3.195)	(-0.855)	(4.039)	(0.554)	(1.803)	(0.127)
${\mathit{MktRet}}_{t-2}$	37.766***	−2.552%	−0.387	−4.224**	−5.050%	−1.929	−0.280
t-stat	(3.459)	(-0.992)	(-1.339)	(-2.065)	(-0.516)	(-0.983)	(-0.039)
${\mathit{MktVol}}_{t-2}$	−0.362	0.412%*	0.331***	−0.194	−0.035%	−0.220	−1.603**
t-stat	(-0.355)	(1.714)	(12.248)	(-1.016)	(-0.038)	(-1.203)	(-2.362)
${\mathit{MktIlliq}}_{t-2}$	0.168	−0.003%	−0.009***	−0.018	−0.083%	0.024	−0.053
t-stat	(1.341)	(-0.105)	(-2.577)	(-0.777)	(-0.734)	(1.062)	(-0.629)
${\mathit{AGGCRet}}_{t-2}$	2.817	0.025%	0.071	0.349	−0.612%	−0.339	−0.067
t-stat	(1.078)	(0.041)	1.029	(0.712)	(-0.261)	(-0.721)	(-0.038)
${\mathit{AGGCVol}}_{t-2}$	−0.044	0.039%	0.007**	−0.026	−0.001%	0.316***	−0.159*
t-stat	(-0.348)	(1.306)	(1.981)	(-1.081)	(-0.007)	(13.732)	(-1.867)
${\mathit{AGGCIlliq}}_{t-2}$	0.056	−0.004%	−0.000	0.006	0.005%	−0.018***	0.132***
t-stat	(1.565)	(-0.493)	(-0.343)	(0.849)	(0.139)	(-2.715)	(5.518)

Notes: The sample has daily observations from 2013-2021. The statistical significance at 1%, 5%, and 10% levels are represented as ***, ** and *, respectively.

Sources: Authors.

Table 4. Ganger causality tests from VAR model.

Causing	Caused	F-Stat.	p-Value
Panel A: Granger causalities from the stock market to the carbon market
MktRV	AGGCRet	0.203	0.816
MktRV	AGGCVol	3.217**	0.040
MktRV	AGGCIlliq	2.109	0.121
MktRet	AGGCRet	0.399	0.671
MktRet	AGGCVol	0.975	0.377
MktRet	AGGCIlliq	0.571	0.565
MktVol	AGGCRet	0.121	0.886
MktVol	AGGCVol	5.147***	0.006
MktVol	AGGCIlliq	7.493***	0.001
MktIlliq	AGGCRet	1.375	0.253
MktIlliq	AGGCVol	2.630*	0.072
MktIlliq	AGGCIlliq	0.198	0.820
Panel B: Granger causalities from the carbon market to the stock market
AGGCRet	MktRV	0.674	0.510
AGGCRet	MktRet	0.071	0.931
AGGCRet	MktVol	0.954	0.385
AGGCRet	MktIlliq	0.340	0.712
AGGCVol	MktRV	0.386	0.680
AGGCVol	MktRet	1.565	0.209
AGGCVol	MktVol	2.456*	0.086
AGGCVol	MktIlliq	0.692	0.501
AGGCIlliq	MktRV	1.377	0.252
AGGCIlliq	MktRet	0.126	0.882
AGGCIlliq	MktVol	0.208	0.812
AGGCIlliq	MktIlliq	0.371	0.690

Notes: The sample has daily observations from 2013-2021. The statistical significance at 1%, 5%, and 10% levels are represented as ***, ** and *, respectively.

Sources: Authors.

Table 5. Descriptive statistics for market variables: Weekly results.

	Mean	Min	Max	Std.dev.	Skewness	Kurtosis	ADF
MktRV	14.998	5.792	87.606	9.789	2.908	12.112	−2.889**
MktRet	0.001	−0.028	0.021	0.006	−0.601	2.801	−17.701***
MktVol	26.066	24.679	27.731	0.588	0.235	0.222	−3.322***
MktIlliq	−31.362	−33.292	−29.705	0.720	−0.040	−0.402	−3.655***
AGGCRet	0.001	−0.100	0.137	0.024	1.126	8.325	−7.118***
AGGCVol	14.273	6.655	16.902	1.521	−1.215	2.836	−5.244***
AGGCIlliq	−16.131	−22.169	0.000	3.092	1.711	5.446	−4.500***

Notes: This table reports the summary statistics for weekly market variables. There are 383 weekly observations. ADF refers to the statistics of stationary tests, and *** represents the statistical significance at the 1% level.

Sources: Authors.

Table 6. Equation results of VAR(1): Weekly results.

Variables	Equations
	${\mathit{MktRV}}_t$	${\mathit{MktRet}}_t$	${\mathit{MktVol}}_t$	${\mathit{MktIlliq}}_t$	${\mathit{AGGCRet}}_t$	${\mathit{AGGCVol}}_t$	${\mathit{AGGCIlliq}}_t$
Intercept	−56.996***	1.320%	1.674***	−9.542***	10.954%	−2.523	10.445
t-stat	(-3.656)	(0.704)	(3.171)	(-5.714)	(1.527)	(-1.015)	(1.562)
${\mathit{MktRV}}_{t-1}$	0.686***	0.002%	−0.001	0.028***	0.028%	−0.003	−0.002
t-stat	(15.754)	(0.307)	(-0.570)	(6.038)	(1.401)	(-0.376)	(-0.132)
${\mathit{MktRet}}_{t-1}$	61.757	10.552%*	8.830***	−13.166***	18.082%	1.937	−36.198*
t-stat	(1.334)	(1.895)	(5.637)	(-2.656)	(0.849)	(0.262)	(-1.824)
${\mathit{MktVol}}_{t-1}$	4.185***	−0.073%	0.945***	−0.701***	−0.889%**	0.185	−0.165
t-stat	(5.099)	(-0.735)	(34.012)	(-7.970)	(-2.355)	(1.412)	(-0.467)
${\mathit{MktIlliq}}_{t-1}$	1.422***	−0.020%	0.009	0.125**	−0.282%	−0.019	0.276
t-stat	(2.707)	(-0.321)	(0.501)	(2.215)	(-1.168)	(-0.224)	(1.227)
${\mathit{AGGCRet}}_{t-1}$	14.889	−1.467%	0.153	1.049	−1.975%	0.947	−2.656
t-stat	(1.345)	(-1.101)	(0.409)	(0.884)	(-0.388)	(0.537)	(-0.560)
${\mathit{AGGCVol}}_{t-1}$	−0.100	−0.001%	0.002	0.009	0.329%***	0.733***	−0.452***
t-stat	(-0.450)	(-0.036)	(0.255)	(0.369)	(3.206)	(20.621)	(-4.726)
${\mathit{AGGCIlliq}}_{t-1}$	0.086	0.002%	−0.002	0.012	0.102%**	−0.061***	0.444***
t-stat	(0.789)	(0.124)	(-0.486)	(1.042)	(2.024)	(-3.480)	(9.493)

Notes: This table presents the results of weekly data for VAR equations in OLS. The sample is from 2013-2021. The statistical significance at 1%, 5%, and 10% levels are represented as ***, ** and *, respectively.

Sources: Authors.

Table 7. Ganger causality tests from VAR model: Weekly results.

Causing	Caused	F-Stat.	p-Value
Panel A: Granger causalities from the stock market to the carbon market
MktRV	AGGCRet	1.962	0.161
MktRV	AGGCVol	0.141	0.707
MktRV	AGGCIlliq	0.017	0.895
MktRet	AGGCRet	0.721	0.396
MktRet	AGGCVol	0.069	0.793
MktRet	AGGCIlliq	3.327*	0.068
MktVol	AGGCRet	5.547**	0.019
MktVol	AGGCVol	1.994	0.158
MktVol	AGGCIlliq	0.219	0.640
MktIlliq	AGGCRet	1.364	0.243
MktIlliq	AGGCVol	0.050	0.822
MktIlliq	AGGCIlliq	1.506	0.220
Panel B: Granger causalities from the carbon market to the stock market
AGGCRet	MktRV	1.808	0.179
AGGCRet	MktRet	1.213	0.271
AGGCRet	MktVol	0.167	0.683
AGGCRet	MktIlliq	0.782	0.377
AGGCVol	MktRV	0.202	0.653
AGGCVol	MktRet	0.001	0.971
AGGCVol	MktVol	0.065	0.799
AGGCVol	MktIlliq	0.136	0.713
AGGCIlliq	MktRV	0.622	0.430
AGGCIlliq	MktRet	0.015	0.901
AGGCIlliq	MktVol	0.237	0.627
AGGCIlliq	MktIlliq	1.085	0.298

Notes: This table presents the results of Granger causality tests from a VAR(1) model using the weekly data. The lag order of the VAR model is determined by the Bayesian information criterion (BIC). The sample is from 2013-2021. The statistical significance at 1%, 5%, and 10% levels are represented as ***, ** and *, respectively.

Sources: Authors.