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 2 Symmetric and asymmetric estimation results for exchange rates 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
\({sp}_{\mathrm{t}-1}\)	− 0.065*** (0.018)	\({sp}_{\mathrm{t}-1}\)	− 0.076*** (0.018)	\({sp}_{\mathrm{t}-1}\)	− 0.068*** (0.019)	\({sp}_{\mathrm{t}-1}\)	− 0.072*** (0.020)
\({reer}_{\mathrm{t}-1}\)	0.021 (0.119)	\({reer}_{\mathrm{t}-1}\)	0.120 (0.113)	\({reer}_{\mathrm{t}-1}\)	0.154 (0.154)	\({reer}_{\mathrm{t}-1}\)	0.168 (0.156)
\({fr}_{\mathrm{t}-1}\)	− 0.028 (0.023)	\({fr}_{\mathrm{t}-1}\)	− 0.033 (0.024)	\({fr}_{\mathrm{t}-1}\)	− 0.016 (0.028)	\({fr}_{\mathrm{t}-1}\)	− 0.020 (0.029)
\({i}_{\mathrm{t}-1}\)	0.002 (0.007)	\({i}_{\mathrm{t}-1}\)	− 0.0002 (0.007)	\({i}_{\mathrm{t}-1}\)	− 0.006 (0.009)	\({i}_{\mathrm{t}-1}\)	− 0.007 (0.009)
\({ipi}_{t-1}\)	0.015 (0.067)	\({ipi}_{t-1}\)	− 0.006 (0.068)	\({ipi}_{t-1}\)	0.036 (0.080)	\({ipi}_{t-1}\)	0.027 (0.082)
\({m2}_{t-1}\)	− 0.007 (0.045)	\({m2}_{t-1}\)	0.006 (0.046)	\({m2}_{t-1}\)	− 0.001 (0.055)	\({m2}_{t-1}\)	0.006 (0.056)
\({\Delta }{sp}_{t-1}\)	0.322*** (0.066)	\({\Delta }{sp}_{t-1}\)	0.309*** (0.067)	\({\Delta }{sp}_{t-1}\)	0.272*** (0.069)	\({\Delta }{sp}_{t-1}\)	0.268** (0.069)
\({\Delta }{sp}_{t-4}\)	0.291*** (0.067)	\({\Delta }{sp}_{t-4}\)	0.293*** (0.068)	\({\Delta }{sp}_{t-4}\)	0.225*** (0.068)	\({\Delta }{sp}_{t-4}\)	0.232** (0.069)
\({\Delta }{fr}_{t-3}\)	− 0.725** (0.365)	\({{\Delta }i}_{t-3}\)	0.005 (0.008)	\({\Delta }{fr}_{t-1}\)	1.099** (0.441)	\({\Delta }{fr}_{t-1}\)	1.084** (0.447)
Constant	0.536 (0.492)	Constant	0.617*** (0.143)	\({\Delta }{fr}_{t-3}\)	− 0.932** (0.439)	\({\Delta }{fr}_{t-3}\)	− 0.921** (0.441)
\({t}_{\mathrm{BDM}}\)	− 3.700	\({t}_{\mathrm{BDM}}\)	− 4.120*	\({\Delta }{i}_{t}\)	− 0.024** (0.011)	\({{\Delta }i}_{t}\)	− 0.023** (0.011)
\({\mathrm{F}}_{\mathrm{PSS}}\)	3.770*	\({\mathrm{F}}_{\mathrm{PSS}}\)	3.666*	Constant	− 0.302 (0.622)	Constant	0.482*** (0.123)
\({\beta}_{neer}\)	0.317	\({\beta}_{neer}\)	1.584	\({t}_{\mathrm{BDM}}\)	− 3.560	\({t}_{\mathrm{BDM}}\)	− 3.658
\({\beta}_{fr}\)	− 0.436	\({\beta}_{fr}\)	− 0.439	\({\mathrm{F}}_{\mathrm{PSS}}\)	2.860	\({\mathrm{F}}_{\mathrm{PSS}}\)	2.903
\({\beta}_{i}\)	0.030	\({\beta}_{i}\)	0.003	\({\beta}_{neer}\)	2.269	\({\beta}_{neer}\)	2.325
\({\beta}_{ipi}\)	0.230	\({\beta}_{ipi}\)	− 0.075	\({\beta}_{fr}\)	− 0.233	\({\beta}_{fr}\)	− 0.280
\({\beta}_{m2}\)	− 0.100	\({\beta}_{m2}\)	0.078	\({\beta}_{i}\)	− 0.094	\({\beta}_{i}\)	− 0.090
		\({W}_{LR,neer}\)	–	\({\beta}_{ipi}\)	0.529	\({\beta}_{ipi}\)	0.374
		\({W}_{SR,neer}\)	–	\({\beta}_{m2}\)	− 0.014	\({\beta}_{m2}\)	0.085
						\({W}_{LR,neer}\)	–
						\({W}_{SR,neer}\)	–
Diagnostic tests statistics
R²	0.293	R²	0.282	R²	0.282	R²	0.277
Adj-R²	0.255	Adj-R²	0.244	Adj-R²	0.236	Adj-R²	0.238
\({\mathrm{\chi }}_{\mathrm{norm}}^{2}\)	7.61** [0.022]	\({\mathrm{\chi }}_{\mathrm{norm}}^{2}\)	8.14** [0.017]	\({\mathrm{\chi }}_{\mathrm{norm}}^{2}\)	19.33*** [0.0001]	\({\mathrm{\chi }}_{\mathrm{norm}}^{2}\)	2.07 [0.356]
\({\mathrm{\chi }}_{\mathrm{sc}}^{2}\)	11.958* [0.063]	\({\mathrm{\chi }}_{\mathrm{sc}}^{2}\)	11.949* [0.063]	\({\mathrm{\chi }}_{\mathrm{sc}}^{2}\)	8.396 [0.211]	\({\mathrm{\chi }}_{\mathrm{sc}}^{2}\)	8.946 [0.177]
\({\mathrm{\chi }}_{\mathrm{HET}}^{2}\)	7.38*** [0.007]	\({\mathrm{\chi }}_{\mathrm{HET}}^{2}\)	0.55 [0.457]	\({\mathrm{\chi }}_{\mathrm{HET}}^{2}\)	6.54** [0.011]	\({\mathrm{\chi }}_{\mathrm{HET}}^{2}\)	0.000 [0.992]
\({\mathrm{\chi }}_{\mathrm{Ra}\mathrm{m}\mathrm{s}\mathrm{e}\mathrm{y}}^{2}\)	0.60 [0.619]	\({\mathrm{\chi }}_{\mathrm{R}\mathrm{a}\mathrm{m}\mathrm{s}\mathrm{e}\mathrm{y}}^{2}\)	0.73 [0.533]	\({\mathrm{\chi }}_{\mathrm{R}\mathrm{a}\mathrm{m}\mathrm{s}\mathrm{e}\mathrm{y}}^{2}\)	1.99 [0.117]	\({\mathrm{\chi }}_{\mathrm{R}\mathrm{a}\mathrm{m}\mathrm{s}\mathrm{e}\mathrm{y}}^{2}\)	1.22 [0.306]
Cusum	Unstable	Cusum	stable	Cusum	Unstable	Cusum	stable

(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) \({\beta}_{neer}\) indicates the symmetric long-run coefficient of the nominal effective exchange rates to the 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), conservative critical values are adopted, k = 5, and the upper bound test statistics at 10%, 5%, and 1% are 3.35, 3.79, and 4.68, respectively; the upper bound \({t}_{\mathrm{B}\mathrm{D}\mathrm{M}}\) test statistics at 10%, 5% and 1% are − 3.86, − 4.19 and − 4.79, 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 hypothesis is \(-{\lambda }_{2}^{+}/{\lambda }_{1}=-{\lambda }_{2}^{-}/{\lambda }_{1}\), while \({W}_{SR}\) refers to the Wald test of short-run symmetry, and the relevant joint null hypothesis is \({\sum }_{j=0}^{n2}{\delta }_{1,j}^{+}={\sum }_{j=0}^{n2}{\delta }_{1,j}^{-}\); (8) standard error and p-values are displayed in parentheses and brackets, respectively (9) Cusum denotes the CUSUM test for the stability of parameters

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