Do the RMB exchange rate and global commodity prices have asymmetric or symmetric effects on China’s stock prices?

Long, Shaobo; Zhang, Mengxue; Li, Keaobo; Wu, Shuyu

doi:10.1186/s40854-021-00262-0

Financial Innovation

Table 3 Symmetric and asymmetric estimation results for exchange rates and commodities prices pass through to stock prices

From: Do the RMB exchange rate and global commodity prices have asymmetric or symmetric effects on China’s stock prices?

Exchange rate → Shanghai Composite Index				Exchange rate → Shenzhen Composite Index
Symmetric ARDL		NARDL		Symmetric ARDL		NARDL
\({sp}_{\mathrm{t}-1}\)	− 0.062*** (0.017)	\({sp}_{\mathrm{t}-1}\)	− 0.085*** (0.017)	\({sp}_{\mathrm{t}-1}\)	− 0.073*** (0.020)	\({sp}_{\mathrm{t}-1}\)	− 0.107*** (0.019)
\({neer}_{\mathrm{t}-1}\)	− 0.193 (0.159)	\({neer}_{\mathrm{t}-1}\)	− 0.661*** (0.180)	\({neer}_{\mathrm{t}-1}\)	− 0.112 (0.189)	\({neer}_{\mathrm{t}-1}\)	− 0.547*** (0.205)
\({comm}_{\mathrm{t}-1}\)	− 0.170*** (0.051)	\({comm}_{\mathrm{t}-1}^{+}\)	− 0.288*** (0.063)	\({comm}_{\mathrm{t}-1}\)	− 0.198*** (0.064)	\({comm}_{\mathrm{t}-1}^{+}\)	− 0.403*** (0.074)
\({fr}_{\mathrm{t}-1}\)	0.074* (0.039)	\({comm}_{\mathrm{t}-1}^{-}\)	− 0.154*** (0.056)	\({fr}_{\mathrm{t}-1}\)	0.095** (0.049)	\({comm}_{\mathrm{t}-1}^{-}\)	− 0.232*** (0.069)
\({i}_{\mathrm{t}-1}\)	0.014* (0.007)	\({fr}_{\mathrm{t}-1}\)	0.104*** (0.039)	\({i}_{\mathrm{t}-1}\)	0.016* (0.008)	\({fr}_{\mathrm{t}-1}\)	0.190*** (0.048)
\({ipi}_{t-1}\)	0.029 (0.066)	\({i}_{\mathrm{t}-1}\)	0.010 (0.007)	\({ipi}_{t-1}\)	0.052 (0.075)	\({i}_{\mathrm{t}-1}\)	0.001 (0.010)
\({m2}_{t-1}\)	− 0.022 (0.046)	\({ipi}_{t-1}\)	− 0.067 (0.067)	\({m2}_{t-1}\)	− 0.022 (0.052)	\({ipi}_{t-1}\)	− 0.006 (0.075)
\({\Delta }{sp}_{t-1}\)	0.235*** (0.065)	\({m2}_{t-1}\)	0.268** (0.114)	\({\Delta }{sp}_{t-4}\)	0.201*** (0.063)	\({m2}_{t-1}\)	0.299** (0.128)
\({\Delta }{comm}_{t-3}\)	− 0.310*** (0.102)	\({\Delta }{sp}_{t-4}\)	0.318*** (0.064)	\({\Delta }{neer}_{t-4}\)	− 1.156** (0.451)	\({\Delta }{sp}_{t-4}\)	0.205*** (0.063)
\({\Delta }{fr}_{t}\)	− 0.799** (0.337)	\({{\Delta }neer}_{\mathrm{t}-5}^{+}\)	1.588** (0.619)	\({comm}_{t-3}\)	− 0.289** (0.127)	\({{\Delta }neer}_{\mathrm{t}-1}^{-}\)	2.025*** (0.748)
\({\Delta }{fr}_{t-1}\)	1.003*** (0.350)	\({{\Delta }neer}_{\mathrm{t}-1}^{-}\)	1.689*** (0.637)	\({\Delta }{comm}_{t-4}\)	− 0.543*** (0.125)	\({{\Delta }neer}_{\mathrm{t}-3}^{-}\)	1.499** (0.706)
Constant	1.730** (0.777)	\({{\Delta }comm}_{\mathrm{t}-1}^{+}\)	0.444*** (0.138)	\({\Delta }{fr}_{t-1}\)	1.570*** (0.390)	\({{\Delta }comm}_{\mathrm{t}-1}^{-}\)	0.405** (0.166)
		\({{\Delta }comm}_{\mathrm{t}-3}^{+}\)	− 0.295* (0.159)	\({\Delta }{fr}_{t-2}\)	0.990** (0.403)	\({{\Delta }comm}_{\mathrm{t}-3}^{-}\)	− 0.372** (0.185)
		\({{\Delta }comm}_{\mathrm{t}-4}^{-}\)	− 0.417*** (0.159)	Constant	1.290 (0.899)	\({{\Delta }comm}_{\mathrm{t}-4}^{-}\)	− 0.599*** (0.181)
		Constant	0.695*** (0.137)			\({\Delta }{fr}_{t-1}\)	1.319*** (0.407)
\({t}_{\mathrm{B}\mathrm{D}\mathrm{M}}\)	− 3.590	\({t}_{\mathrm{B}\mathrm{D}\mathrm{M}}\)	− 4.869**			\({\Delta }{fr}_{t-2}\)	1.162*** (0.401)
\({\mathrm{F}}_{\mathrm{P}\mathrm{S}\mathrm{S}}\)	3.640**	\({\mathrm{F}}_{\mathrm{P}\mathrm{S}\mathrm{S}}\)	5.891***			\({\Delta }{i}_{t}\)	− 0.021** (0.010)
\({\beta}_{neer}\)	− 3.108	\({\beta}_{neer}\)	− 7.772***			Constant	0.653*** (0.124)
\({\beta}_{comm}\)	− 2.742***	\({\beta}_{{comm}^{+}}\)	− 3.386***	\({t}_{\mathrm{B}\mathrm{D}\mathrm{M}}\)	− 3.750	\({t}_{\mathrm{B}\mathrm{D}\mathrm{M}}\)	− 5.528***
\({\beta}_{fr}\)	1.198**	\({\beta}_{{comm}^{-}}\)	− 1.815***	\({\mathrm{F}}_{\mathrm{P}\mathrm{S}\mathrm{S}}\)	3.270*	\({\mathrm{F}}_{\mathrm{P}\mathrm{S}\mathrm{S}}\)	6.437***
\({\beta}_{i}\)	0.225*	\({W}_{LR,neer}\)	–	\({\beta}_{neer}\)	− 1.526	\({\beta}_{neer}\)	− 5.104***
\({\beta}_{ipi}\)	0.467	\({W}_{SR,neer}\)	0.017	\({\beta}_{comm}\)	− 2.697***	\({\beta}_{{comm}^{+}}\)	− 3.755***
\({\beta}_{m2}\)	− 0.350	\({W}_{LR,comm}\)	4.18**	\({\beta}_{fr}\)	1.290**	\({\beta}_{{comm}^{-}}\)	− 2.168***
		\({W}_{SR,comm}\)	1.235	\({\beta}_{i}\)	0.218*	\({W}_{LR,neer}\)	–
		\({\beta}_{fr}\)	1.227***	\({\beta}_{ipi}\)	0.710	\({W}_{SR,neer}\)	10.43***
		\({\beta}_{i}\)	0.122	\({\beta}_{m2}\)	− 0.306	\({W}_{LR,comm}\)	5.354**
		\({\beta}_{ipi}\)	− 0.793			\({W}_{SR,comm}\)	3.932**
		\({\beta}_{m2}\)	3.148**			\({\beta}_{fr}\)	1.768***
						\({\beta}_{i}\)	0.011
						\({\beta}_{ipi}\)	− 0.060
						\({\beta}_{m2}\)	2.790**
Diagnostic tests statistics
R²	0.358	R²	0.435	R²	0.427	R²	0.487
Adj-R²	0.316	Adj-R²	0.387	Adj-R²	0.383	Adj-R²	0.432
\({\mathrm{\chi }}_{\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}}^{2}\)	2.77 [0.250]	\({\mathrm{\chi }}_{\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}}^{2}\)	6.07** [0.048]	\({\mathrm{\chi }}_{\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}}^{2}\)	9.75** [0.008]	\({\mathrm{\chi }}_{\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}}^{2}\)	2.86 [0.239]
\({\mathrm{\chi }}_{\mathrm{s}\mathrm{c}}^{2}\)	4.011 [0.675]	\({\mathrm{\chi }}_{\mathrm{s}\mathrm{c}}^{2}\)	11.326* [0.079]	\({\mathrm{\chi }}_{\mathrm{s}\mathrm{c}}^{2}\)	7.222 [0.301]	\({\mathrm{\chi }}_{\mathrm{s}\mathrm{c}}^{2}\)	10.259 [0.114]
\({\mathrm{\chi }}_{\mathrm{H}\mathrm{E}\mathrm{T}}^{2}\)	0.02 [0.877]	\({\mathrm{\chi }}_{\mathrm{H}\mathrm{E}\mathrm{T}}^{2}\)	2.37 [0.124]	\({\mathrm{\chi }}_{\mathrm{H}\mathrm{E}\mathrm{T}}^{2}\)	1.35 [0.245]	\({\mathrm{\chi }}_{\mathrm{H}\mathrm{E}\mathrm{T}}^{2}\)	0.000 [0.955]
\({\mathrm{\chi }}_{\mathrm{R}\mathrm{a}\mathrm{m}\mathrm{s}\mathrm{e}\mathrm{y}}^{2}\)	0.50 [0.684]	\({\mathrm{\chi }}_{\mathrm{R}\mathrm{a}\mathrm{m}\mathrm{s}\mathrm{e}\mathrm{y}}^{2}\)	0.98 [0.403]	\({\mathrm{\chi }}_{\mathrm{R}\mathrm{a}\mathrm{m}\mathrm{s}\mathrm{e}\mathrm{y}}^{2}\)	2.43* [0.067]	\({\mathrm{\chi }}_{\mathrm{R}\mathrm{a}\mathrm{m}\mathrm{s}\mathrm{e}\mathrm{y}}^{2}\)	0.76 [0.517]
Cusum	stable	Cusum	Stable	Cusum	Stable	Cusum	Stable

Notes: (1)***, ** and * denote significance at the 1%, 5%, and 10% levels, respectively; (2) maximum lag lengths of \({n}_{k}\) and \({l}_{k}\) are 6, and the general-to-specific approach is used to decide the final specifications by dropping all insignificant variables; (3) \({\beta}_{neer}\) and \({\beta}_{comm}\) indicate the symmetric long-run coefficients of the exchange rates and global commodity prices on China’s stock prices; \({\beta}_{{comm}^{+}}\) and \({\beta}_{{comm}^{-}}\) refer to the rising and falling long-run coefficients of global commodity prices on China’s stock prices; (5)\({\mathrm{F}}_{\mathrm{P}\mathrm{S}\mathrm{S}}\) indicates the Paseran-Shin-Smith F test statistic (2001), and following Shin et al. (2014), the conservative of critical values is adopted, k = 6, and the upper bound test statistics at 10%, 5% and 1% are 3.23, 3.61 and 4.43, respectively; the upper bound of \({t}_{\mathrm{B}\mathrm{D}\mathrm{M}}\) test statistics at 10%, 5%, and 1% are − 4.04, − 4.38 and− 4.99, respectively; (6)\({\mathrm{\chi }}_{\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}}^{2}\), \({\mathrm{\chi }}_{sc}^{2}\), \({\mathrm{\chi }}_{HET}^{2}\), and \({\mathrm{\chi }}_{Ramsey}^{2}\) denote the test of Normality, the Breusch-Godfrey LM test for serial autocorrelation, the Breusch-Pagan test for heteroskedasticity, and the Ramsey RESET test, respectively; (7)\({W}_{LR}\) refers to the Wald test for long-run symmetry, the relevant joint null hypotheses are \(-{\lambda }_{2}^{+}/{\lambda }_{1}=-{\lambda }_{2}^{-}/{\lambda }_{1}\) and \(-{\lambda }_{3}^{+}/{\lambda }_{1}=-{\lambda }_{3}^{-}/{\lambda }_{1}\) respectively, while \({W}_{SR}\) refers to the Wald test of short-run symmetry and the relevant joint null hypotheses are \({\sum }_{j=0}^{I2}{\delta }_{2,j}^{+}={\sum }_{j=0}^{I2}{\delta }_{2,j}^{-}\) and \({\sum }_{j=0}^{I2}{\tau }_{2,j}^{+}={\sum }_{j=0}^{I2}{\tau }_{2,j}^{-}\) respectively; (8) standard error and p-values are displayed in parentheses and brackets, respectively; (9) Cusum denotes the CUSUM and CUSUM squared test for stability of parameters

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