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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. (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