Document Type : Article

**Authors**

^{1}
School of Mathematics and Economics, Hubei University of Education, Wuhan 430205, P. R. China

^{2}
School of Science, Wuhan University of Technology, Wuhan 430070, P. R. China

^{3}
School of Mathematics and Statistics, Huanggang Normal University, Huanggang 438000, P. R. China

**Abstract**

The implementation of the Shenzhen-Hong Kong Stock Connect (SHSC) mechanism has realized the largest two-way opening of China's capital market, but also increased the transmission of risks. In order to analyze the impact of SHSC on the volatility of single market in Shenzhen or Hong Kong, this paper establishes the volatility models of stock markets in Shenzhen and Hong Kong based on the GARCH-type models with different perturbation terms. The pre-applicable test is made and the result shows that the return rate series of Shenzhen and Hong Kong stock markets are stable and heteroscedastic, and they meet the conditions of establishing the GARCH-type models. Then, the GARCH model and EGARCH model are established to analyze the volatility of stock markets in Shenzhen and Hong Kong respectively. The results show that the opening of SHSC has increased the short-term volatility of the stock markets in Shenzhen and Hong Kong and improved the efficiency of information transmission between these two stock markets. Moreover, influenced by SHSC, the leverage effect of Shenzhen stock market is increasing, while that of Hong Kong stock market is decreasing.

**Keywords**

References:

1. Cao, F.Q. Reform, innovation and risk prevention of China's capital market", Finance Forum, 23(9), pp. 5{10 (2018).

2. Gui, H., Market Expectations of Shenzhen-Hong Kong Stock Connect, China Finance, 17, pp. 74{75 (2016).

3. Wang, Q.Y. and Chong, T.T.L. Co-integrated or not? after the Shanghai-Hong Kong and Shenzhen-Hong Kong stock connection schemes", Economics Letters, 163, pp. 167{171 (2018).

4. Rao, C.J., Xiao, X.P., Goh, M., et al. Compound mechanism design of supplier selection based on multiattribute

auction and risk management of supply chain", Computers & Industrial Engineering, 105, pp. 63{75 (2017).

5. Qu, S.J., Zhou, Y.Y., Zhang, Y.L., et al. Optimal strategy for a green supply chain considering shipping policy and default risk", Computers and Industrial Engineering, 131, pp. 172{186 (2019)

6. Engle, R.F. Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. infalation", Econometrica, 50(4), pp. 987{1008 (1982).

7. Engle, R.F. and Victor, K.N. Measuring and testing the impact of news on volatility", Journal of Finance, 48(5), pp. 1022{1082 (1993).

8. Engle, R.F., Lilien, D.M., and Robins, R.P. Estimating time varying risk premia in the term structure: the ARCH-M model", Econometrica, 55(2), pp. 391{407 (1987).

9. Abdalla, S.Z.S. Modelling exchange rate volatility using GARCH models: empirical evidence from Arab countries", International Journal of Economics and Finance, 4(3), pp. 216{229 (2012).

10. Omolo, S.A. Modelling exchange rate volatility of KES/USD using GARCH-type models", Journal of Finance, 10(3), pp. 47{59 (2014).

11. Dai, Z.F., Zhou, H.T., Wen, F.H., et al. Efficient predictability of stock return volatility: The role of C. Wang et al./Scientia Iranica, Transactions E: Industrial Engineering 29 (2022) 372{386 385

stock market implied volatility", The North American

Journal of Economics and Finance, 52, p. 101174

(2020).

12. Zhang, T.H., Yuan, Y., and Wu, X. Is microblogging

data re

ected in stock market volatility? Evidence

from Sina Weibo", Finance Research Letters, 32, p.

101173 (2020).

13. Baklaci, H.F., Aydogan, B., and Yelkenci, T. Impact

of stock market trading on currency market volatility

spillovers", Research in International Business and

Finance, 52, p. 101182 (2020).

14. Kawakatsu, H. Matrix exponential GARCH", Journal

of Econometrics, 134(1), pp. 95{128 (2006).

15. Glosten, L.R., Jagannathan, R., and Runkle, D.E.

On the relation between the expected value and the

volatility of the nominal excess return on stocks",

Journal of Finance, 38(5), pp. 1779{1801 (1993).

16. Zakoian, J.M. Threshold heteroskedastic models",

Journal of Economic Dynamics and Control, 18(5),

pp. 931{955 (1994).

17. Baillie, R.T., Bollerslev, T., and Mikkelsen, H.O.

Fractionally integrated generalized autoregressive

conditional heteroskedasticity", Journal of Econometrics,

74(1), pp. 3{30 (1996).

18. Bollerslev, T. A conditionally heteroskedastic time

series model for speculative prices and rates of return",

Review of Economics and Statistics, 69(3), pp. 542{

547 (1987).

19. Nelson, D.B. 3-conditional heteroskedasticity in asset

returns: a new approach", Econometrica, 59(2), pp.

37{64 (1996).

20. Fornari, F. and Mele, A. Modeling the changing

asymmetry of conditional variances", Economics Letters,

50(2), pp. 197{203 (1996).

21. Laopodis, N.T. Monetary policy implications of

volatility linkages among long-term interest rates",

Journal of Economics and Finance, 24(2), pp. 160{

179 (2000).

22. Hung, J.C. Intelligent threshhold GARCH model

applied to stock market of transmissions that volatility",

Proceedings of the International Conference on

Convergence Information Technology, pp. 1523{1528

(2007).

23. Yu, H.H., Fang, L.B., and Sun, W.C. Forecasting

performance of global economic policy uncertainty

for volatility of Chinese stock market", Physica A:

Statistical Mechanics and Its Applications, 505(3), pp.

931{940 (2018).

24. Zhou, X.H. and Huang, Y. Research on the volatility

relationship between the stock markets of Shanghai

and Shenzhen", Economics and Management Research,

8, pp. 77{82 (2008).

25. Jiang, T.H. Reversal of asymmetric e ect of stock

market volatility in China - An empirical study based

on VS-GARCH model", Modern Finance and Economics,

2, pp. 65{68 (2008).

26. Guo, H. Analysis of Chinese stock market volatility:

an empirical study based on RS-GARCH model",

Financial Theory & Practice, 2, pp. 78{80 (2014).

27. Wang, M. and Wang, C.L. Research on conditional

value at risk based on return volatility and heavy

tail - Evidence from CSI 300 index", Mathematics in

Practice and Theory, 47(17), pp. 70{76 (2017).

28. Zheng, Z.X., Wang, H.R., and Zhu, F.M. Studies

on volatility features and jump behavior of Shanghai

50ETF market based on Levy-GARCH model", Chinese

Journal of Management Science, 27(2), pp. 41{52

(2019).

29. Kim, H.Y. andWon, C.H. Forecasting the volatility of

stock price index: A hybrid model integrating LSTM

with multiple GARCH-type models", Expert Systems

with Applications, 1031, pp. 25{37 (2018).

30. Wang, L., Ma, F., Liu, J., et al. Forecasting

stock price volatility: New evidence from

the GARCH-MIDAS model", International Journal

of Forecasting, 36(2), pp. 684{694 (2020). DOI:

https://doi.org/10.1016/j.ijforecast. 2019.08 .005

31. Rao, C.J., Goh, M., Zhao, Y., et al. Location selection

of sustainability city logistics centers", Transportation

Research Part D: Transport and Environment, 36, pp.

29{44 (2015).

32. Xiao, Q.Z., Chen, L., Xie, M., et al. Optimal contract

design in sustainable supply chain: interactive impacts

of fairness concern and overcon dence", Journal of the

Operational Research Society, 72(7), pp. 1505{1524

(2021). DOI: 10.1080/01605682.2020.1727784

33. Zhou, W. and Xu, Z.S. Envelopment analysis, preference

fusion, and membership improvement of intuitionistic

fuzzy numbers", IEEE Transactions on

Fuzzy Systems, 28(9), pp. 2119{2130 (2020). DOI:

10.1109/TFUZZ.2019.2930483

34. Rao, C.J., Lin, H., and Liu, M. Design of comprehensive

evaluation index system for P2P credit risk of

three rural" borrowers", Soft Computing, 24(15), pp.

11493{11509 (2020). DOI: 10.1007/s00500-019-04613-z

35. Wang, M.T., Lu, L., and Song, K. Impacts of policy

factors on volatility of stock markets", Journal of

Management Sciences in China, 15(12), pp. 40{57

(2012).

36. Liu, J., Ma, F., and Zhang, Y.J. Forecasting the

Chinese stock volatility across global stock markets",

Physica A: Statistical Mechanics and Its Applications,

525, pp. 466{477 (2019).

37. Liu, L. and Pan, Z.Y. Forecasting stock market

volatility: The role of technical variables", Economic

Modelling, 84, pp. 55{65 (2020).

386 C. Wang et al./Scientia Iranica, Transactions E: Industrial Engineering 29 (2022) 372{386

38. Tian, C. and Peng, J.J. An integrated picture fuzzy

ANP-TODIM multi-criteria decision-making approach

for tourism attraction recommendation", Technological

and Economic Development of Economy, 26(2), pp.

331{354 (2020). DOI: https://doi.org/10.3846/tede.

2019.11412

39. Corbet, S., Gurdgiev, C., and Meegan, A. Longterm

stock market volatility and the in

uence of

terrorist attacks in Europe", The Quarterly Review of

Economics and Finance, 68, pp. 118{131 (2018).

40. Adel Rastkhiz, S.E., Mobini Dehkordi, A., Jahangiri

Farsi, Y., et al. A new approach to evaluating

entrepreneurial opportunities", Journal of Small Business

and Enterprise Development, 26(1), pp. 67{84

(2018).

41. Fang, L.B., Guo, B.S., and Zeng, Y. The e ect of

skewness parameter on the performance of GARCH

family when making estimation and prediction", Statistical

Research, 31(10), pp. 106{112 (2014).

42. Li, F.S. Does margin trading aggravate stock market

1. Cao, F.Q. Reform, innovation and risk prevention of China's capital market", Finance Forum, 23(9), pp. 5{10 (2018).

2. Gui, H., Market Expectations of Shenzhen-Hong Kong Stock Connect, China Finance, 17, pp. 74{75 (2016).

3. Wang, Q.Y. and Chong, T.T.L. Co-integrated or not? after the Shanghai-Hong Kong and Shenzhen-Hong Kong stock connection schemes", Economics Letters, 163, pp. 167{171 (2018).

4. Rao, C.J., Xiao, X.P., Goh, M., et al. Compound mechanism design of supplier selection based on multiattribute

auction and risk management of supply chain", Computers & Industrial Engineering, 105, pp. 63{75 (2017).

5. Qu, S.J., Zhou, Y.Y., Zhang, Y.L., et al. Optimal strategy for a green supply chain considering shipping policy and default risk", Computers and Industrial Engineering, 131, pp. 172{186 (2019)

6. Engle, R.F. Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. infalation", Econometrica, 50(4), pp. 987{1008 (1982).

7. Engle, R.F. and Victor, K.N. Measuring and testing the impact of news on volatility", Journal of Finance, 48(5), pp. 1022{1082 (1993).

8. Engle, R.F., Lilien, D.M., and Robins, R.P. Estimating time varying risk premia in the term structure: the ARCH-M model", Econometrica, 55(2), pp. 391{407 (1987).

9. Abdalla, S.Z.S. Modelling exchange rate volatility using GARCH models: empirical evidence from Arab countries", International Journal of Economics and Finance, 4(3), pp. 216{229 (2012).

10. Omolo, S.A. Modelling exchange rate volatility of KES/USD using GARCH-type models", Journal of Finance, 10(3), pp. 47{59 (2014).

11. Dai, Z.F., Zhou, H.T., Wen, F.H., et al. Efficient predictability of stock return volatility: The role of C. Wang et al./Scientia Iranica, Transactions E: Industrial Engineering 29 (2022) 372{386 385

stock market implied volatility", The North American

Journal of Economics and Finance, 52, p. 101174

(2020).

12. Zhang, T.H., Yuan, Y., and Wu, X. Is microblogging

data re

ected in stock market volatility? Evidence

from Sina Weibo", Finance Research Letters, 32, p.

101173 (2020).

13. Baklaci, H.F., Aydogan, B., and Yelkenci, T. Impact

of stock market trading on currency market volatility

spillovers", Research in International Business and

Finance, 52, p. 101182 (2020).

14. Kawakatsu, H. Matrix exponential GARCH", Journal

of Econometrics, 134(1), pp. 95{128 (2006).

15. Glosten, L.R., Jagannathan, R., and Runkle, D.E.

On the relation between the expected value and the

volatility of the nominal excess return on stocks",

Journal of Finance, 38(5), pp. 1779{1801 (1993).

16. Zakoian, J.M. Threshold heteroskedastic models",

Journal of Economic Dynamics and Control, 18(5),

pp. 931{955 (1994).

17. Baillie, R.T., Bollerslev, T., and Mikkelsen, H.O.

Fractionally integrated generalized autoregressive

conditional heteroskedasticity", Journal of Econometrics,

74(1), pp. 3{30 (1996).

18. Bollerslev, T. A conditionally heteroskedastic time

series model for speculative prices and rates of return",

Review of Economics and Statistics, 69(3), pp. 542{

547 (1987).

19. Nelson, D.B. 3-conditional heteroskedasticity in asset

returns: a new approach", Econometrica, 59(2), pp.

37{64 (1996).

20. Fornari, F. and Mele, A. Modeling the changing

asymmetry of conditional variances", Economics Letters,

50(2), pp. 197{203 (1996).

21. Laopodis, N.T. Monetary policy implications of

volatility linkages among long-term interest rates",

Journal of Economics and Finance, 24(2), pp. 160{

179 (2000).

22. Hung, J.C. Intelligent threshhold GARCH model

applied to stock market of transmissions that volatility",

Proceedings of the International Conference on

Convergence Information Technology, pp. 1523{1528

(2007).

23. Yu, H.H., Fang, L.B., and Sun, W.C. Forecasting

performance of global economic policy uncertainty

for volatility of Chinese stock market", Physica A:

Statistical Mechanics and Its Applications, 505(3), pp.

931{940 (2018).

24. Zhou, X.H. and Huang, Y. Research on the volatility

relationship between the stock markets of Shanghai

and Shenzhen", Economics and Management Research,

8, pp. 77{82 (2008).

25. Jiang, T.H. Reversal of asymmetric e ect of stock

market volatility in China - An empirical study based

on VS-GARCH model", Modern Finance and Economics,

2, pp. 65{68 (2008).

26. Guo, H. Analysis of Chinese stock market volatility:

an empirical study based on RS-GARCH model",

Financial Theory & Practice, 2, pp. 78{80 (2014).

27. Wang, M. and Wang, C.L. Research on conditional

value at risk based on return volatility and heavy

tail - Evidence from CSI 300 index", Mathematics in

Practice and Theory, 47(17), pp. 70{76 (2017).

28. Zheng, Z.X., Wang, H.R., and Zhu, F.M. Studies

on volatility features and jump behavior of Shanghai

50ETF market based on Levy-GARCH model", Chinese

Journal of Management Science, 27(2), pp. 41{52

(2019).

29. Kim, H.Y. andWon, C.H. Forecasting the volatility of

stock price index: A hybrid model integrating LSTM

with multiple GARCH-type models", Expert Systems

with Applications, 1031, pp. 25{37 (2018).

30. Wang, L., Ma, F., Liu, J., et al. Forecasting

stock price volatility: New evidence from

the GARCH-MIDAS model", International Journal

of Forecasting, 36(2), pp. 684{694 (2020). DOI:

https://doi.org/10.1016/j.ijforecast. 2019.08 .005

31. Rao, C.J., Goh, M., Zhao, Y., et al. Location selection

of sustainability city logistics centers", Transportation

Research Part D: Transport and Environment, 36, pp.

29{44 (2015).

32. Xiao, Q.Z., Chen, L., Xie, M., et al. Optimal contract

design in sustainable supply chain: interactive impacts

of fairness concern and overcon dence", Journal of the

Operational Research Society, 72(7), pp. 1505{1524

(2021). DOI: 10.1080/01605682.2020.1727784

33. Zhou, W. and Xu, Z.S. Envelopment analysis, preference

fusion, and membership improvement of intuitionistic

fuzzy numbers", IEEE Transactions on

Fuzzy Systems, 28(9), pp. 2119{2130 (2020). DOI:

10.1109/TFUZZ.2019.2930483

34. Rao, C.J., Lin, H., and Liu, M. Design of comprehensive

evaluation index system for P2P credit risk of

three rural" borrowers", Soft Computing, 24(15), pp.

11493{11509 (2020). DOI: 10.1007/s00500-019-04613-z

35. Wang, M.T., Lu, L., and Song, K. Impacts of policy

factors on volatility of stock markets", Journal of

Management Sciences in China, 15(12), pp. 40{57

(2012).

36. Liu, J., Ma, F., and Zhang, Y.J. Forecasting the

Chinese stock volatility across global stock markets",

Physica A: Statistical Mechanics and Its Applications,

525, pp. 466{477 (2019).

37. Liu, L. and Pan, Z.Y. Forecasting stock market

volatility: The role of technical variables", Economic

Modelling, 84, pp. 55{65 (2020).

386 C. Wang et al./Scientia Iranica, Transactions E: Industrial Engineering 29 (2022) 372{386

38. Tian, C. and Peng, J.J. An integrated picture fuzzy

ANP-TODIM multi-criteria decision-making approach

for tourism attraction recommendation", Technological

and Economic Development of Economy, 26(2), pp.

331{354 (2020). DOI: https://doi.org/10.3846/tede.

2019.11412

39. Corbet, S., Gurdgiev, C., and Meegan, A. Longterm

stock market volatility and the in

uence of

terrorist attacks in Europe", The Quarterly Review of

Economics and Finance, 68, pp. 118{131 (2018).

40. Adel Rastkhiz, S.E., Mobini Dehkordi, A., Jahangiri

Farsi, Y., et al. A new approach to evaluating

entrepreneurial opportunities", Journal of Small Business

and Enterprise Development, 26(1), pp. 67{84

(2018).

41. Fang, L.B., Guo, B.S., and Zeng, Y. The e ect of

skewness parameter on the performance of GARCH

family when making estimation and prediction", Statistical

Research, 31(10), pp. 106{112 (2014).

42. Li, F.S. Does margin trading aggravate stock market

uctuation? From the perspective of asymmetric

volatility", Journal of Financial Research, 440(2), pp.

147{162 (2017).

43. Lee, O. V-uniform ergodicity of a continuous time

asymmetric power GARCH(1,1) model", Statistics &

Probability Letters, 82(4), pp. 812{817 (2012).

44. Yang, H. and Wu, X.G. Semiparametric EGARCH

model with the case study of China stock market",

Economic Modelling, 28(3), pp. 761{766 (2011).

45. Do, A., Powell, R. Yong, J., et al. Time-varying asymmetric

volatility spillover between global markets and

China's A, B and H-shares using EGARCH and DCCEGARCH

models", The North American Journal of

Economics and Finance, 54, p. 101096 (2020). DOI:

https://doi.org/10.1016/j.najef.2019.101096

volatility", Journal of Financial Research, 440(2), pp.

147{162 (2017).

43. Lee, O. V-uniform ergodicity of a continuous time

asymmetric power GARCH(1,1) model", Statistics &

Probability Letters, 82(4), pp. 812{817 (2012).

44. Yang, H. and Wu, X.G. Semiparametric EGARCH

model with the case study of China stock market",

Economic Modelling, 28(3), pp. 761{766 (2011).

45. Do, A., Powell, R. Yong, J., et al. Time-varying asymmetric

volatility spillover between global markets and

China's A, B and H-shares using EGARCH and DCCEGARCH

models", The North American Journal of

Economics and Finance, 54, p. 101096 (2020). DOI:

https://doi.org/10.1016/j.najef.2019.101096

Transactions on Industrial Engineering (E)

January and February 2022Pages 372-386