Dynamic heterogeneous panel analysis of the correlation between stock prices and exchange rates

Figures & data

Table 1. Summary of literature on the relationship between stock price and exchange rate.

Author(s)	Method or approach	Sample Countries	Period	Traditional Approach (E→S the positive relationship between the exchange rate and the stock price)	Portfolio Approach (S→E the negative relationship between the exchange rate and the stock price)
Bahmani-Oskooee and Sohrabian(1992)	causality tests	Unite State	1973M7–1988M12	none	The bi-directional causality between stock prices and the exchange rate in short-run
Smith (1992)	optimising inter-temporal model of asset choice	Germany and Japan	1974Q1–1989Q3	none	Germany (The negative impact of the stock prices on the exchange rate) Japan (The positive impact of the stock prices on the exchange rate)
Mok (1993)	ARIMA causality tests	Hong Kong	1986/04/2–1991/06/30	Hong Kong: short-run. (sub-period)	Hong Kong: short-run. (sub-period)
Ajayi and Mougoue (1996)	ECM model	Canada, France, US, UK, Italy, Japan, Germany and Netherlands	1985M04–1991M07	The positive impact of the stock prices on the exchange rate in long- run	The negative impact of the stock prices on the exchange rate in short-run
Abdalla and Murinde (1997)	VECM model	India, Pakistan, Philippines, and South Korea	1985/1/1–1994/7/31	India, Pakistan, and South Korea	Philippines: in the short-run
Wu (2000)	VECM model	Singapore	1980M01–1996M12	none	Singapore: short-run
Granger et al. (2000)	VAR model Granger causality test	Hong Kong, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore, Thailand, and Taiwan	1986/1/3–1998/6/16	South Korea: in the short-run	Hong Kong, Malaysia, Philippines, Singapore, Thailand, and Taiwan: in the short-run
Nieh and Lee (2001)	VECM model	G-7 countries	1993/10/1–1996/2/15	None	None
Doong et al. (2005)	Vector GARCH-M	Indonesia, Korea, Malaysia,Philippines, Thailand, and Taiwan	1989/01/6–2003/01/3	Indonesia, Malaysia, Philippines, Thailand, (in the short-run)	Indonesia, Malaysia, Philippines, Thailand: in the short-run
Phylaktis and Ravazzolo (2005)	Co-integration test Granger causality tests	Hong Kong, Malaysia, Singapore, Thailand and Philippines	1980M01–1998M12	All countries: in the short-run	None
Fernandez (2006)	Tail -dependence test	US, Japan, the Czech Republic, Poland, Switzerland, and the UK	1999M1–2002M12	None	None
Yau and Nieh(2006)	Co-integration test Granger causality tests	Taiwan and Japan	19991M01–2005M07	Taiwan: in the long-run	Taiwan and Japan: in the short-run
Pan et al. (2007)	VAR model Granger causality tests	Hong Kong, Japan, Korea, Malaysia, Singapore, Taiwan, and Thailand,	1988M1–1998M10	Hong Kong, Japan, Malaysia, and Thailand: in the short-run	Hong Kong, Korea, and Singapore: in the short-run
Yau and Nieh (2009)	Threshold ECM model Granger causality tests	Taiwan and Japan	1991M01–2008M03	Taiwan: in the long-run	None
Chen and Chen (2011)	VECM model, Bounds test, and linear and nonlinear Causality	G-7 countries	1974M01–2007M09	US, Canada, Italy, Germany, and the UK (in nonlinear causality). Germany and Japan (in nonlinear causality)	US, Canada, Italy, Germany, and the UK (in nonlinear causality). US, Canada, Italy, Germany, and the UK. (in linear causality)
Walid et al. (2011)	Markov-Switching EGARCH	Hong Kong, Singapore, Malaysia and Mexico	1994M12–2009M3	All countries, in the high mean-low variance regime. The stock price volatility reacts asymmetrically to events in the FX market

Source: Authors’ compilation.

Table 2. The pooling results of the basic statistics of the stock price and exchange rate.

Variable	ln stit	ln exit	Δ ln stit	Δ ln exit
Mean	7.99	2.57	0.01	0.00
Maximum	11.16	9.37	2.48	1.08
Minimum	4.36	−0.71	−3.37	−0.21
Standard deviation	1.47	2.25	0.18	0.06
Skewness	−0.09	1.06	−3.82	4.32
Kurtosis	2.15	3.83	132.87	71.74
Observation	1,334	1,334	1,305	1,305

Notes:

the notation ‘Δ’ denotes the first-difference term.

Source: Authors' analysis

Table 3. Estimation results of the dynamic panel model.

Methods	PMG	MG	Hausman test	DFE
Dependent variable Δ ln stit
Error-correction effect (coefficient)			H0: PMG
ϕi	−0.054^***	−0.094^***	H1: MG	−0.063^***
	(0.011)	(0.014)	Chi-square statistic	(0.009)
Long-run effect (coefficient)			0.100
-θ1i	1.241^***	−1.690	[0.758]	−0.246
	(0.369)	(9.020)		(0.474)
Short-run effect (coefficient)
-δ11i	−1.028^***	−0.708^***		−0.503^***
	(0.223)	(0.203)		(0.078)
Constant (μi)	0.254^***	1.586		0.554^***
	(0.046)	(1.108)		(0.113)
Country number	29	29		29
Observation	1,305	1,305		1,305

Notes:

The parenthesis (.) is the standard deviation, [.] is the P-value; and

^*** denotes significance at the 1% level. The model is Δ ln st_it = ϕ_i(ln st_i,t-1 – θ_1i ln ex_it) – δ_11iΔ ln ex_it + μ_i + ɛ_it.

Source: Authors' analysis.

Table 4. Estimation results of the dynamic panel model (the pan-European Dow Jones STOXX 600 index is used to replace the German DAX index).

Methods	PMG	MG	Hausman test	DFE
Dependent variable Δ ln stit
Error-correction effect (coefficient)			H0: PMG
ϕi	−0.054^***	−0.094^***	H1: MG	−0.063^***
	(0.011)	(0.014)	Chi-square statistic	(0.009)
Long-run effect (coefficient)			0.10
-θ1i	1.241^***	−1.688	[0.758]	−0.246
	(0.369)	(9.020)		(0.474)
Short-run effect (coefficient)
−δ11i	−1.028^***	−0.707^***		−0.503^***
	(0.224)	(0.203)		(0.078)
Constant (μi)	0.254^***	1.586		0.553^***
	(0.046)	(1.108)		(0.113)
Country number	29	29		29
Observation	1,305	1,305		1,305

Notes:

In this table, the pan-European Dow Jones STOXX 600 index is used to replace the German DAX index for a second empirical verification, coupled with the euro exchange rate, to verify whether the findings of this paper are robust. The parenthesis (.) is the standard deviation, [.] is the P-value; and

^*** denotes significance at the 1% level. The model is Δ ln st_it = ϕ_i(ln st_i,t-1 − θ_1i ln ex_it) − δ_11iΔ ln ex_it + μ_i + ɛ_it.

Source: Authors' analysis.

Table 5. Estimation results of the dynamic panel model – ARDL (2,2) model.

Methods	PMG	MG	Hausman test	DFE
Dependent variable Δ ln stit
Error-correction effect (coefficient)			H0: PMG
ϕi	−0.073^***	−0.138^***	H1: MG	−0.077^***
	(0.013)	(0.024)	Chi-square statistic	(0.009)
Long-run effect (coefficient)			0.05
−θ1i	1.221^***	0.205	[0.824]	−0.010
	(0.284)	(4.360)		(0.410)
Short-run effect (coefficient)
−	−1.011^***	−0.742^***		−0.452^***
	(0.274)	(0.162)		(0.784)
−	−0.110	0.039		−0.164^**
	(0.761)	(0.147)		(0.796)
−	0.157^***	0.169^***		0.132^***
	(0.234)	(0.032)		(0.028)
Constant (μi)	0.364^***	1.605^**		0.630^***
	(0.066)	(0.764)		(0.118)
Country number	29	29		29
Observation	1,249	1,249		1,249

Notes:

The parenthesis (.) is the standard deviation, [.] is the P-value;

^** , and

^*** denotes significance at the 5%, and 1% level, respectively. The model is ARDL(2, 2): .

Source: Authors' analysis.

Table 6. Estimation results of the dynamic panel model for 25 countries – ARDL (1, 1) model.

Methods	PMG	MG	Hausman test	DFE
Dependent variable Δ ln stit
Error-correction effect (coefficient)			H0: PMG
ϕi	−0.059^***	−0.097^***	H1: MG	−0.065^***
	(0.012)	(0.016)	Chi-square statistic	(0.009)
Long-run effect (coefficient)			0.06
−θ1i	1.356^***	−1.441	[0.800]	0.150
	(0.372)	(10.491)		(0.541)
Short-run effect (coefficient)
−δ11i	−1.120^***	−0.759^***		−0.509^***
	(0.254)	(0.232)		(0.893)
Constant (μi)	0.254^***	1.679		0.505^***
	(0.048)	(1.285)		(0.120)
Country number	25	25		25
Observation	1,125	1,125		1,125

Notes:

The parenthesis (.) is the standard deviation, [.] is the P-value;

^*** denotes significance at the 1% level. The model is ARDL (1, 1). This table removes the data of four countries from the data of the 29 countries (Czech, Denmark, Hungary and Poland).

Source: Authors' analysis.

Table 7. Estimation results of the dynamic panel model for 25 countries – ARDL (2, 2) model.

Methods	PMG	MG	Hausman test	DFE
Dependent variable Δ ln stit
Error-correction effect (coefficient)			H0: PMG
ϕi	−0.071^***	−0.127^***	H1: MG	−0.079^***
	(0.014)	(0.021)	Chi-square statistic	(0.010)
Long-run effect (coefficient)			0.21
−θ1i	1.291^***	−2.144	[0.645]	−0.087
	(0.302)	(7.102)		(0.463)
Short-run effect (coefficient)
−	−1.087^***	−0.637^***		−0.443^***
	(0.303)	(0.238)		(0.898)
−	−0.142	0.129		−0.184^**
	(0.875)	(0.217)		(0.911)
−	0.129^***	0.132^***		0.123^***
	(0.214)	(0.030)		(0.030)
Constant (μi)	0.317^***	1.979		0.624^***
	(0.059)	(1.293)		(0.125)
Country number	25	25		25
Observation	1,100	1,100		1,100

Notes:

The parenthesis (.) is the standard deviation, [.] is the P-value;

^*** denotes significance at the 1% level. The model is ARDL (2, 2): . This table removes the data of four countries from the data of the 29 countries (Czech, Denmark, Hungary and Poland).

Source: Authors' analysis.

Table 8. Estimation results of the dynamic panel model during 2002Q3–2006Q4 (pre-crisis).

Methods	PMG	MG	Hausman test	DFE
Dependent variable Δ ln stit
Error-correction effect (coefficient)			H0: PMG
ϕi	−0.063^***	−0.164^***	H1: MG	−0.072^***
	(0.020)	(0.038)	Chi-square statistic	(0.016)
Long-run effect (coefficient)			0.98
−θ1i	3.529^***	1.004	[0.323]	−0.955
	(0.673)	(2.642)		(1.000)
Short-run effect (coefficient)
−δ11i	−1.155^**	−0.402		−0.088
	(0.515)	(0.600)		(0.134)
Constant (μi)	−0.042	2.338^***		0.811^***
	(0.119)	(0.764)		(0.289)
Country number	29	29		29
Observation	493	493		493

Notes:

The parenthesis (.) is the standard deviation, [.] is the P-value;

^** , and

^*** denotes significance at the 5%, and 1% level, respectively.

Source: Authors' analysis.

Table 9. Estimation results of the dynamic panel model during 2007Q1–2011Q2 (post-crisis).

Methods	PMG	MG	Hausman test	DFE
Dependent variable Δ ln stit
Error-correction effect (coefficient)			H0: PMG
ϕi	−0.154^***	−0.253^***	H1: MG	−0.191^***
	(0.022)	(0.026)	Chi-square statistic	(0.026)
Long-run effect (coefficient)			0.76
−θ1i	2.491^***	0.008	[0.384]	0.859^**
	(0.481)	(2.892)		(0.426)
Short-run effect (coefficient)
−δ11i	−1.472^***	−1.334^***		−1.126^***
	(0.246)	(0.258)		(0.091)
Constant (μi)	0.422^***	1.001		1.179^***
	(0.140)	(0.835)		(0.340)
Country number	29	29		29
Observation	493	493		493

Notes:

The parenthesis (.) is the standard deviation, [.] is the P-value;

^** , and

^*** denotes significance at the 5%, and 1% level, respectively.

Source: Authors' analysis.

Table 10. Estimation results of the dynamic panel model with ex is the NEER (measure by TWI).

Methods	PMG	MG	Hausman test	DFE
Dependent variable Δ ln stit
Error-correction effect (coefficient)			H0: PMG
ϕi	−0.064^***	−0.085^***	H1: MG	−0.069^***
	(0.014)	(0.017)	Chi-square statistic	(0.010)
Long-run effect (coefficient)			2.04
−θ1i	1.666^***	−3.237	[0.154]	−1.579
	(0.266)	(3.242)		(0.887)
Short-run effect (coefficient)
−δ11i	−0.084	0.049		−0.577^***
	(0.313)	(0.308)		(0.153)
Constant (μi)	0.023	1.292		1.078^***
	(0.030)	(0.686)		(0.279)
Country number	24	24		24
Observation	1,080	1,080		1,305

Notes:

The parenthesis (.) is the standard deviation, [.] is the P-value;

^*** denotes significance at the 1% level. The effective exchange rate index (NEER) that measure by TWI are taken from the IFS database of IMF. The model is Δ ln st_it = ϕ_i(ln st_i,t-1 - θ_1i ln ex_it) - δ_11iΔ ln ex_it + μ_i + ɛ_it.

Source: Authors' analysis.

Table 11. Estimation results of the dynamic panel model sub-periods for the 13 OECD countries.

Methods	PMG	MG	Hausman test
Dependent variable Δ ln stit
Error-correction effect (coefficient)			H0: PMG
ϕi	−0.099^***	−0.125^***	H1: MG
	(0.017)	(0.152)	Chi-square statistic
Long-run effect (coefficient)			0.99
−θ1i	−1.360^***	−0.602	[0.319]
	(0.225)	(0.772)
Short-run effect (coefficient)
−δ11i	−0.295^**	−0.321^**
	(0.149)	(0.151)
Constant (μi)	0.982^***	1.146^***
	(0.145)	(0.200)
Country number	13	13
Observation	585	585

Notes:

The parenthesis (.) is the standard deviation, [.] is the P-value;

^** , and

^*** denotes significance at the 5%, and 1% level, respectively. The 13 OECD countries are Australia, Canada, Czech, Denmark, Germany, Hungary, Japan, South Korea, Mexico, Norway, Sweden, Switzerland, and England, respectively.

Source: Authors' analysis.

Table 12. Estimation results of the dynamic panel model sub-periods for the 16 non-OECD countries.

Methods	PMG	MG	Hausman test
Dependent variable Δ ln stit
Error-correction effect (coefficient)			H0: PMG
ϕi	−0.047^***	−0.069^***	H1: MG
	(0.117)	(0.211)	Chi-square statistic
Long-run effect (coefficient)			0.08
−θ1i	2.146^***	−2.571	[0.783]
	(0.696)	(16.576)
Short-run effect (coefficient)
−δ11i	−1.497^***	−1.022^***
	(0.360)	(0.332)
Constant (μi)	0.124^***	1.943
	(0.047)	(2.027)
Country number	16	16
Observation	720	720

Notes:

The parenthesis (.) is the standard deviation, [.] is the P-value;

^** , and

^*** denotes significance at the 5%, and 1% level, respectively. The 16 non-OECD countries is Argentina, Brazil, China, Chile, Hong Kong, Indonesia, India, Israel, Malaysia, the Philippines, Poland, Russia, South Africa, Singapore, Thailand, and Taiwan, respectively.

Source: Authors' analysis.

Table

Periods: 2000Q1--2011Q2
No	Code	Nation	Stock price index (st)	Exchange rate (ex)
1	ARG	Argentina	Merval Argentina Index	1. The exchange rate in this paper is measured by the conversion of USD 1 into units of local currency. Therefore, when ex value rises (declines), it represents the depreciation (appreciation) of the local currency
2	AUS	Australia (OECD)	Sydney All Ords Stock Index
3	BRA	Brazil	Brazil BOYESPA Index
4	CAN	Canada (OECD)	Toronto Stock Exchange Venture Composite Index	2. The exchange rate regimes of the sample countries are:
5	CHA	China	Shanghai Stock Exchange Composite Index	A. Managed floating with no predetermined path for the exchange rate: Argentina, Czech, India, Malaysia, Russian, Singapore, Thailand, and Taiwan
6	CHI	Chile	IPSA Index	B. Independently floating: Australia, Brazil, Canada, Indonesia, Israel, Japan, South Korea, Mexico, Norway, the Philippines, Poland, South Africa, Sweden, Switzerland, and the UK
7	CZE	Czech (OECD)	PRAGUE SE PX
8	DEN	Denmark (OECD)	OMX COPENHAGEN 20 Index
9	GER	Germany (OECD)	German DAX IndexDow Jones STOXX 600 index
10	HK	Hong Kong	HK Hange Seng Index	C. Other conventional fixed peg arrangements: China and Chile
11	HUN	Hungary (OECD)	BUDAPEST (BUX) index
12	IDO	Indonesia	Indonesia JSX index	D. Pegged exchange rates within horizontal bands: Denmark and Hungary
13	IND	India	BSE SENSEX 30 Index	E. Exchange arrangements with no separate legal tender: Germany
14	ISR	Israel	TA100 index
15	JAP	Japan (OECD)	NK-225 Index (Tokyo)
16	KOR	South Korea (OECD)	South Korea Stock Index	F. Currency board arrangements: Hong Kong
17	MAL	Malaysia	Kuala Lumpur Stock Index
18	MEX	Mexico (OECD)	Mexico IPC Index
19	NOR	Norway (OECD)	Oslo OBX Stock Indexes
20	PHI	The Philippines	Manila Stock Index
21	POL	Poland	Warsaw GENERAL Index
22	RUS	Russian	RTS Stock Index
23	SAF	South Africa	Johannesburg Stock Index
24	SNG	Singapore	Singapal Strait Times Index
25	SWE	Sweden (OECD)	Stockholm Affa. Sto Index
26	SWL	Switzerland (OECD)	Zurich Market Index
27	THA	Thailand	Bangkok Set Stock Index
28	TWN	Taiwan	Taiwan TSE Index
29	UK	United Kingdom (OECD)	London FTSE 100 Index

Table

Nation	The Chinn-Ito index (KAOPEN) (Note 1)
year	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011
Argentina	1.38	−1.17	−1.17	−0.11			−0.8
Australia	1.12
Brazil	−1.17	−1.17	−0.11			0.15	0.41				0.15	−0.11
Canada	2.44, most financially open
China	−1.17
Chile	−1.17	1.38	1.65	1.91	2.18	2.44			2.18	1.91	1.65	1.38
Czech	0.15	1.65	1.91	2.18	2.44, most financially open
Denmark	2.44, most financially open
Germany	2.44, most financially open
Hong Kong	2.44, most financially open
Hungary	−0.11	1.38	1.65	1.91	2.18	2.44, most financially open
Indonesia	1.12											−0.11
India	−1.17
Israel	1.38	1.38	1.65	1.65	1.91	2.18	2.44, most financially open
Japan	2.44, most financially open
South Korea	−0.11									0.15	0.41	0.68
Malaysia	−0.11								1.12	−0.11	−1.17	−1.17
Mexico	1.12	1.12	0.06	0.06	1.12
Norway	2.44, most financially open
Philippines	0.06										−1.17	−1.17
Poland	−1.17		0.06
Russian	−0.81	−1.86	−0.11							0.15	0.41	0.68
South Africa	−1.16
Singapore	2.44, most financially open
Sweden	2.44, most financially open
Switzerland	2.44, most financially open
Thailand	−0.11							−1.17	−0.11	−1.17
Taiwan	NA (financially open started date at 1987 in Taiwan, Note 2)
United Kingdom	2.44, most financially open

Bahmani-Oskooee, M., & Sohrabian, A. (1992). Stock prices and the effective exchange rate of the dollar. Applied Economics, 24, 459–464.10.1080/00036849200000020

 Web of Science ®Google Scholar

Smith, C. (1992). Stock markets and the exchange rate: A mutli-country approach. Journal of Macroeconomics, 14, 607–629.10.1016/0164-0704(92)90003-Q

 Web of Science ®Google Scholar

Mok, H. (1993). Causality of interest rate, exchange rate and stock price at stock market open and close in Hong Kong. Asia Pacific Journal of Management, 10, 123–143.10.1007/BF01734274

 Google Scholar

Ajayi, R. A., & Mougoue, M. (1996). On the dynamic relation between stock prices and exchange rates. Journal of Financial Research, 19, 193–207.10.1111/jfir.1996.19.issue-2

 Google Scholar

Abdalla, I. S. A., & Murinde, V. (1997). Exchange rate and stock price interactions in emerging financial markets: Evidence on India, Korea, Pakistan and the Philippines. Applied Financial Economics, 7, 25–35.10.1080/096031097333826

 Google Scholar

Wu, Y. (2000). Stock prices and exchange rates in a VEC model-the case of Singapore in the 1990s. Journal of Economics and Finance, 24, 260–274.10.1007/BF02752607

 Google Scholar

Granger, C. W. J., Huang, B. N., & Yang, C. W. (2000). A bivariate causality between stock prices and exchange rates: Evidence from recent Asian Flu. The Quarter Review of Economics and Finance, 40, 337–354.10.1016/S1062-9769(00)00042-9

 Google Scholar

Nieh, C. C., & Lee, C. F. (2001). Dynamic relationship between stock prices and exchange rates for G-7 countries. The Quarterly Review and Finance, 41, 477–490.10.1016/S1062-9769(01)00085-0

 Google Scholar

Doong, S. C., Yang, S. Y., & Wang, A. T. (2005). The dynamic relationship and pricing of stocks and exchange rates: Empirical evidence from Asian emerging markets. The Journal of American Academy of Business, Cambridge, 7, 118–123.

 Google Scholar

Phylaktis, K., & Ravazzolo, F. (2005). Stock prices and exchange rate dynamics. Journal of International Money and Finance, 24, 1031–1053.10.1016/j.jimonfin.2005.08.001

 Web of Science ®Google Scholar

Fernandez, V. (2006). External dependence in European capital markets. Journal of Applied Economics, 9, 275–293.

 Web of Science ®Google Scholar

Yau, H. Y., & Nieh, C. C. (2006). Interrelations among stock prices of Taiwan and Japan and NTD/Yen exchange rate. Journal of Asian Economics, 17, 535–552.10.1016/j.asieco.2006.04.006

 Google Scholar

Pan, M. S., Fok, R. C. W., & Liu, Y. A. (2007). Dynamic linkages between exchange rates and stock prices: Evidence from East Asian markets. International Review of Economics and Finance, 16, 503–520.10.1016/j.iref.2005.09.003

 Google Scholar

Yau, H. Y., & Nieh, C. C. (2009). Testing for cointegration with threshold effect between stock prices and exchange rates in Japan and Taiwan. Japan and the World Economy, 21, 292–300.10.1016/j.japwor.2008.09.001

 Web of Science ®Google Scholar

Chen, S. W., & Chen, T. C. (2011). The causal relationship between stock prices and exchange rates: Evidence from the G-7. Journal of Economics and Management, 7, 101–133. (in Chinese).

 Google Scholar

Walid, C., Chaker, A., Masood, O., & Fry, J. (2011). Stock market volatility and exchange rates in emerging countries: A Markov-state switching approach. Emerging Markets Review, 12, 272–292.10.1016/j.ememar.2011.04.003

 Web of Science ®Google Scholar