Issue
Authors retain the copyright without restrictions for their published content in this journal. HSSR is a SHERPA ROMEO Green Journal.
Publishing License
This is an open-access article distributed under the terms of
MODELLING THE CONDITIONAL CO-MOVEMENTS OF PAKISTAN AND INTERNATIONAL STOCK MARKETS
Corresponding Author(s) : Ali Akbar Pirzado
Humanities & Social Sciences Reviews,
Vol. 9 No. 3 (2021): May
Abstract
Purpose of the study: This study assesses and evaluates the conditional co-movements and dynamic conditional correlation of the Pakistan Stock Exchange (PSX) with other Stock Market.
Methodology: DCC-GARCH model has been applied due to its feasibility to model the covariance as a function of correlation and variance together.
Main findings: The findings of the research suggest that the Pakistani Stock Exchange (PSX) is highly volatile compared to two other selected stock markets. In-sample fitting, the study has selected the DCC-GARCH (1, 1) model based on information criterion, conversely, the criterion used for out-of-sample forecast evaluation such as MSFE, RMSFE, MAPE, selected the DCC (2,1)-GARCH (1,1).
Application of the study: This study is very useful for the Pakistan stock market and other international selected stocks markets until and unless the government of Pakistan and other governments will devise new policies which may open new opportunities to investors.
Novelty/ Originality of the study: This study has a great potential in the Pakistani stock market to offer investors to several foreign and domestic investors, allowing them to hold Pakistan as well as foreign and local stocks all major benefits.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
- Ahlgren, N., & Antell, J. (2010). Stock market linkages and financial contagion: A cobreaking analysis. The Quarterly Review of Economics and Finance, 50(2), 157-166. https://doi.org/10.1016/j.qref.2009.12.004 DOI: https://doi.org/10.1016/j.qref.2009.12.004
- Alvi, M. A., & Chughtai, S. H. (2015). Co-Movement of Pakistan Stock Market with the Stock Markets of Major Developed Countries which have Portfolio Investment in Pakistan. Management Studies and Economic Systems, 2(1), 72–84. https://doi.org/10.12816/0018084 DOI: https://doi.org/10.12816/0018084
- Andersson-Säll, T., & Lindskog, J. (2019). A Study on the Dcc-Garch Model’s Forecasting Ability with Value-At-Risk Applications on the Scandinavian Foreign Exchange Market. urn:nbn:se:uu:diva-375201
- Anjum, S. (2020). Impact of market anomalies on stock exchange: a comparative study of KSE and PSX. Future Business Journal, 6(1), 1-11. https://doi.org/10.1186/s43093-019-0006-4 DOI: https://doi.org/10.1186/s43093-019-0006-4
- Bala D. A. and Takimoto T., (2017). Stock Markets Volatility Spillovers during Financial Crises: A DCC-MGARCH with Skewed-t Density Approach. Borsa istanbul Rev. https://doi.org/10.1016/j.bir .2017.02.002 DOI: https://doi.org/10.1016/j.bir.2017.02.002
- Baur, N. (2019, June). Linearity vs. Circularity? On Some Common Misconceptions on the Differences in the Research Process in Qualitative and Quantitative Research. Frontiers in Education, 4, 53. https://doi.org/10.3389/feduc.2019.00053 DOI: https://doi.org/10.3389/feduc.2019.00053
- Brunori, P., Peragine, V., & Serlenga, L. (2019). Upward and downward bias when measuring inequality of opportunity. Social Choice and Welfare, 52(4), 635-661. https://doi.org/10.1007/s00355-018-1165-x DOI: https://doi.org/10.1007/s00355-018-1165-x
- Chevallier, J., Nguyen, D. K., Siverskog, J., & Uddin, G. S. (2018). Market integration and financial linkages among stock markets in Pacific Basin countries. Journal of Empirical Finance, 46, 77-92. https://doi.org/10.1016/j.jempfin.2017.12.006 DOI: https://doi.org/10.1016/j.jempfin.2017.12.006
- Cont, R. (2019). Volatility Clustering in Financial Markets : Empirical Facts and Agent-Based Models Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models. May 2005. https://doi.org/10.2139/ssrn.1411462 DOI: https://doi.org/10.2139/ssrn.1411462
- Daelemans, B., Daniels, J. P., & Nourzad, F. (2018). Free trade agreements and volatility of stock returns and exchange rates: evidence from NAFTA. Open Economies Review, 29(1), 141-163. https://doi.org/10.100 7/s11079-017-9472-x DOI: https://doi.org/10.1007/s11079-017-9472-x
- Das, D., & Manoharan, K. (2019). Emerging stock market co-movements in South Asia: wavelet approach. International Journal of Managerial Finance, 15(2), 236-256. https://doi.org/10.1108/IJMF-11-2017-0255 DOI: https://doi.org/10.1108/IJMF-11-2017-0255
- Das, D., Bhowmik, P., & Jana, R. K. (2018). A multiscale analysis of stock return co-movements and spillovers: Evidence from Pacific developed markets. Physica A: Statistical Mechanics and its Applications, 502, 379-393. https://doi.org/10.1016/j.physa.2018.02.143 DOI: https://doi.org/10.1016/j.physa.2018.02.143
- Ehrhardt, M., Teng, L., Ehrhardt, M., & Michael, G. (2016). Fachbereich Mathematik und Naturwissenschaften This version : June 2015 The Dynamic Correlation Model and its Application to the Heston Model. July 2019. https://doi.org/10.1007/978-3-319-33446-2 DOI: https://doi.org/10.1007/978-3-319-33446-2
- Engle, R. F. (2000). University of California , San Diego Department of Economics Dynamic conditional correlation – A simple class of multivariate garch models by discussion paper 2000-09 may 2000 dynamic conditional correlation – a simple class of multivariate garch models Robert F . Engle July 1999 Revised May 2000. https://doi.org/10.2139/ssrn.236998 DOI: https://doi.org/10.2139/ssrn.236998
- Engle, R. F. and Sheppard, K. (2001). Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH. NBER Working, Paper No. 8554. https://doi.org/10.3386/w8554 DOI: https://doi.org/10.3386/w8554
- Engle, R. F., & Patton, A. J. (2001). What good is a volatility model? Quantitative Finance. Institute of Physics Publishing, 1, 237-245. https://doi.org/10.1088/1469-7688/1/2/305 DOI: https://doi.org/10.1088/1469-7688/1/2/305
- Evans, T., & McMillan, D. G. (2009). Financial co-movement and correlation: Evidence from 33 international stock market indices. International Journal of Banking, Accounting and Finance, 1(3), 215–241. https://doi.org/10.1504/IJBAAF.2009.022711 DOI: https://doi.org/10.1504/IJBAAF.2009.022711
- Ewing, B. T., & Seyfried, W. (2003). Modeling The Philips Curve: A Time-Varying Volatility Approach. Applied Econometrics and International Development, 3(2).
- Fan, J., Wang, M., & Yao, Q. (2008). Modelling multivariate volatilities via conditionally uncorrelated components. Journal of the Royal Statistical Society: series B (statistical methodology), 70(4), 679-702. https://doi.org/10.1111/j.1467-9868.2008.00654.x DOI: https://doi.org/10.1111/j.1467-9868.2008.00654.x
- Ferreira, P. (2017). Portuguese and Brazilian stock market integration: a non-linear and detrended approach. Portuguese Economic Journal, 16(1), 49-63. https://doi.org/10.1007/s10258-017-0127-z DOI: https://doi.org/10.1007/s10258-017-0127-z
- Fildes, R., & Petropoulos, F. (2015). Simple versus complex selection rules for forecasting many time series. Journal of Business Research, 68(8), 1692-1701. https://doi.org/10.1016/j.jbusres.2015.03.028 DOI: https://doi.org/10.1016/j.jbusres.2015.03.028
- Ghufran, B., Awan, H. M., Khakwani, A. K., & Qureshi, M. A. (2016). What causes stock market volatility in Pakistan? Evidence from the field. Economics Research International, pg-9. https://doi.org/10.1155/2 016/3698297 DOI: https://doi.org/10.1155/2016/3698297
- Gkillas, K., Tsagkanos, A., & Vortelinos, D. I. (2019). Integration and risk contagion in financial crises: Evidence from international stock markets. Journal of Business Research, 104, 350-365. https://doi.org/10.101 6/j.jbusres.2019.07.031 DOI: https://doi.org/10.1016/j.jbusres.2019.07.031
- Hanif, M. (2020). Relationship between oil and stock markets: Evidence from Pakistan stock exchange. International Journal of Energy Economics and Policy, 10(5), 150. https://doi.org/10.324 79/ijeep.9653 DOI: https://doi.org/10.32479/ijeep.9653
- Herbert, W. E., Ugwuanyi, G. O., & Nwaocha, E. I. (2019). Volatility clustering, leverage effects and risk-return trade-off in the Nigerian stock market. Journal of Finance and Economics, 7(1), 1-13.
- Hkiri, B., Hammoudeh, S., Aloui, C., & Shahbaz, M. (2018). The interconnections between US financial CDS spread and control variables: New evidence using partial and multivariate wavelet coherences. International Review of Economics & Finance, 57, 237-257. https://doi.org/10.1016/j.iref.2018.01.011 DOI: https://doi.org/10.1016/j.iref.2018.01.011
- Iqbal, J. (2014). Stock Market in Pakistan : An Overview Stock Market in Pakistan : An Overview Stock Market in Pakistan : An Overview By Javed Iqbal Department of Econometrics and Business Statistics , Monash Department of Statistics, University of Karachi, Pakistan Postal Address: Department of Statistics, Karachi University, Karachi 75270, Pakistan. May. https://doi.org/10.1177/097265271101100103 DOI: https://doi.org/10.1177/097265271101100103
- Ismail, M. T., Audu, B., & Tumala, M. M. (2016). Comparison of forecasting performance between MODWT-GARCH (1, 1) and MODWT-EGARCH (1, 1) models: Evidence from African stock markets. The Journal of Finance and Data Science, 2(4), 254-264. https://doi.org/10.1016/j.jfds.2017.03.001 DOI: https://doi.org/10.1016/j.jfds.2017.03.001
- Iyiegbuniwe, W., Ezike, J. E., & Amah, P. N. (2012). Heteroskedasticity of Market Return : A Look at the All Nigerian Stock Exchange Index Time Series. 7(16), 13–30. https://doi.org/10.5539/ijbm.v7n16p13 DOI: https://doi.org/10.5539/ijbm.v7n16p13
- Jiang, Y., Yu, M., & Hashmi, S. M. (2017). The financial crisis and co-movement of global stock markets—A case of six major economies. Sustainability, 9(2), 260. https://doi.org/10.3390/su9020260 DOI: https://doi.org/10.3390/su9020260
- Joyo, A. S. (2019). Stock Market Integration of Pakistan with Its Trading Partners : A Multivariate DCC-GARCH Model Approach. https://doi.org/10.3390/su11020303 DOI: https://doi.org/10.3390/su11020303
- Khan, M. I., Akhter, W., & Bhutta, U. (2020). Interest Rate Exposure and Stocks Returns during Global Financial Crisis: Evidence from Islamic and Conventional Markets. Journal of Islamic Business and Management, 10(1), 131-147. https://doi.org/10.26501/jibm/2020.1001-009 DOI: https://doi.org/10.26501/jibm/2020.1001-009
- Khan, M. N., Fifield, S. G., Tantisantiwong, N., & Power, D. M. (2021). Changes in co-movement and risk transmission between South Asian stock markets amidst the development of regional cooperation. Financial Markets and Portfolio Management, 1-31. https://doi.org/10.1007/s11408-021-00386-4 DOI: https://doi.org/10.1007/s11408-021-00386-4
- Kizys, R., & Pierdzioch, C. (2009). Changes in the international comovement of stock returns and asymmetric macroeconomic shocks. Journal of International Financial Markets, Institutions and Money, 19(2), 289–305. https://doi.org/10.1016/j.intfin.2008.01.002 DOI: https://doi.org/10.1016/j.intfin.2008.01.002
- Lal, S. (2019). Determinants of Integration of Stock Markets: A Study of Pakistan and its Trading Partners (Doctoral dissertation, CAPITAL UNIVERSITY).
- Lim, S. J., & Masih, M. (2017). Exploring portfolio diversification opportunities in Islamic capital markets through bitcoin: evidence from MGARCH-DCC and Wavelet approaches. Munich Personal RePEc Archive, MPRA Paper 2017. Available online: https://mpra.ub.uni-muenchen.de/id/eprint/79752 (accessed on 10 August 2018).
- Nasreen, S., Asif, S., Naqvi, A., Tiwari, A. K., & Hammoudeh, S. (2020). A Wavelet-Based Analysis of the Co-Movement between Sukuk Bonds and Shariah Stock Indices in the GCC Region: Implications for Risk Diversification. Gifr 2019, 1–22. DOI: https://doi.org/10.3390/jrfm13040063
- Nguyen, T. H. T. B., & Lam, A. H. (2017). Financial development, international trade, and stock market integration: Evidence in six southeastern Asia countries. Journal of Economics and Development, 19(3), 5-17. https://doi.org/10.33301/JED.2017.19.03.01 DOI: https://doi.org/10.33301/JED.2017.19.03.01
- Panda, A. K., & Nanda, S. (2018). Time-varying synchronization and dynamic conditional correlation among the stock market returns of leading South American economies. International Journal of Managerial Finance. https://doi.org/10.1108/IJMF-11-2016-0206 DOI: https://doi.org/10.1108/IJMF-11-2016-0206
- Paramati, S. R., Roca, E., & Gupta, R. (2016). Economic integration and stock market dynamic linkages: evidence in the context of Australia and Asia. Applied Economics, 48(44), 4210-4226. https://doi.org/10.1080/00036846.2016.1153794 DOI: https://doi.org/10.1080/00036846.2016.1153794
- Paramati, S. R., Zakari, A., Jalle, M., Kale, S., & Begari, P. (2018). The dynamic impact of bilateral trade linkages on stock market correlations of Australia and China. Applied Economics Letters, 25(3), 141-145. https://doi.org/10.1080/13504851.2017.1305067 DOI: https://doi.org/10.1080/13504851.2017.1305067
- Piljak, V. (2013). Essays on the co-movement dynamics of frontier/emerging and developed financial markets. Printed with kind permission of John Wiley and Sons.
- Pirzado, A. A., Jatoi, I. K., Pirzado, M. B., Khaskali, S. A., Chohan, R., & Phulpoto, S. N. (2020). An econometric analysis among different markets of pakistan using dcc-garch model. PalArch's Journal of Archaeology of Egypt/Egyptology, 17(7), 16352-16359.
- Rangel, J. G., & Engle, R. F. (2012). The Factor–Spline–GARCH model for high and low-frequency correlations. Journal of Business & Economic Statistics, 30(1), 109-124. https://doi.org/10.1080 /07350015.2012.643132 DOI: https://doi.org/10.1080/07350015.2012.643132
- Sakti, M. R. P., Masih, M., Saiti, B., & Tareq, M. A. (2018). Unveiling the diversification benefits of Islamic equities and commodities. Managerial Finance, 44(6), 830-850. https://doi.org/10.1108/MF-08-2017-0278 DOI: https://doi.org/10.1108/MF-08-2017-0278
- Savva, C. S. (2009). International stock markets interactions and conditional correlations. Journal of International Financial Markets, Institutions and Money, 19(4), 645-661. https://doi.org/10.1016/ j.intfin.2008.11.001 DOI: https://doi.org/10.1016/j.intfin.2008.11.001
- Swiler, L., Urbina, A., & Williams, B. (2011, March). Multiple Model Inference: Calibration, Selection, and Prediction with Multiple Models. In 52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 19th AIAA/ASME/AHS Adaptive Structures Conference 13t (p. 1844). https://doi.org/10.2514/6.2011-1844 DOI: https://doi.org/10.2514/6.2011-1844
- Teng, L., Ehrhardt, M., & Günther, M. (2016). The dynamic correlation model and its application to the Heston model. Springer Proceedings in Mathematics and Statistics, 165(November), 437–449. https://doi.org/10.100 7/978-3-319-33446-2_21 DOI: https://doi.org/10.1007/978-3-319-33446-2_21
- Trivedi, J., Spulbar, C., Birau, R., & Mehdiabadi, A. (2021). Modelling volatility spillovers, cross-market correlation and co-movements between stock markets in European Union: an empirical case study. Business, Management and Economics Engineering, 19(1), 70-90. https://doi.org/10.3846/bmee.2021.13588 DOI: https://doi.org/10.3846/bmee.2021.13588
- Uddin, G. S. (2013). Co-movements between Germany and International Stock Markets: Some New Evidence from DCC-GARCH and Wavelet Approaches. 1–23. Available at SSRN 2311724. ttps://doi.org/10.2139/ ssrn.2311724 DOI: https://doi.org/10.2139/ssrn.2311724
- Vacha, L., & Barunik, J. (2012). Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis. Energy Economics, 34(1), 241–247. https://doi.org/10.1016/j.eneco.2011.10.007 DOI: https://doi.org/10.1016/j.eneco.2011.10.007
References
Ahlgren, N., & Antell, J. (2010). Stock market linkages and financial contagion: A cobreaking analysis. The Quarterly Review of Economics and Finance, 50(2), 157-166. https://doi.org/10.1016/j.qref.2009.12.004 DOI: https://doi.org/10.1016/j.qref.2009.12.004
Alvi, M. A., & Chughtai, S. H. (2015). Co-Movement of Pakistan Stock Market with the Stock Markets of Major Developed Countries which have Portfolio Investment in Pakistan. Management Studies and Economic Systems, 2(1), 72–84. https://doi.org/10.12816/0018084 DOI: https://doi.org/10.12816/0018084
Andersson-Säll, T., & Lindskog, J. (2019). A Study on the Dcc-Garch Model’s Forecasting Ability with Value-At-Risk Applications on the Scandinavian Foreign Exchange Market. urn:nbn:se:uu:diva-375201
Anjum, S. (2020). Impact of market anomalies on stock exchange: a comparative study of KSE and PSX. Future Business Journal, 6(1), 1-11. https://doi.org/10.1186/s43093-019-0006-4 DOI: https://doi.org/10.1186/s43093-019-0006-4
Bala D. A. and Takimoto T., (2017). Stock Markets Volatility Spillovers during Financial Crises: A DCC-MGARCH with Skewed-t Density Approach. Borsa istanbul Rev. https://doi.org/10.1016/j.bir .2017.02.002 DOI: https://doi.org/10.1016/j.bir.2017.02.002
Baur, N. (2019, June). Linearity vs. Circularity? On Some Common Misconceptions on the Differences in the Research Process in Qualitative and Quantitative Research. Frontiers in Education, 4, 53. https://doi.org/10.3389/feduc.2019.00053 DOI: https://doi.org/10.3389/feduc.2019.00053
Brunori, P., Peragine, V., & Serlenga, L. (2019). Upward and downward bias when measuring inequality of opportunity. Social Choice and Welfare, 52(4), 635-661. https://doi.org/10.1007/s00355-018-1165-x DOI: https://doi.org/10.1007/s00355-018-1165-x
Chevallier, J., Nguyen, D. K., Siverskog, J., & Uddin, G. S. (2018). Market integration and financial linkages among stock markets in Pacific Basin countries. Journal of Empirical Finance, 46, 77-92. https://doi.org/10.1016/j.jempfin.2017.12.006 DOI: https://doi.org/10.1016/j.jempfin.2017.12.006
Cont, R. (2019). Volatility Clustering in Financial Markets : Empirical Facts and Agent-Based Models Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models. May 2005. https://doi.org/10.2139/ssrn.1411462 DOI: https://doi.org/10.2139/ssrn.1411462
Daelemans, B., Daniels, J. P., & Nourzad, F. (2018). Free trade agreements and volatility of stock returns and exchange rates: evidence from NAFTA. Open Economies Review, 29(1), 141-163. https://doi.org/10.100 7/s11079-017-9472-x DOI: https://doi.org/10.1007/s11079-017-9472-x
Das, D., & Manoharan, K. (2019). Emerging stock market co-movements in South Asia: wavelet approach. International Journal of Managerial Finance, 15(2), 236-256. https://doi.org/10.1108/IJMF-11-2017-0255 DOI: https://doi.org/10.1108/IJMF-11-2017-0255
Das, D., Bhowmik, P., & Jana, R. K. (2018). A multiscale analysis of stock return co-movements and spillovers: Evidence from Pacific developed markets. Physica A: Statistical Mechanics and its Applications, 502, 379-393. https://doi.org/10.1016/j.physa.2018.02.143 DOI: https://doi.org/10.1016/j.physa.2018.02.143
Ehrhardt, M., Teng, L., Ehrhardt, M., & Michael, G. (2016). Fachbereich Mathematik und Naturwissenschaften This version : June 2015 The Dynamic Correlation Model and its Application to the Heston Model. July 2019. https://doi.org/10.1007/978-3-319-33446-2 DOI: https://doi.org/10.1007/978-3-319-33446-2
Engle, R. F. (2000). University of California , San Diego Department of Economics Dynamic conditional correlation – A simple class of multivariate garch models by discussion paper 2000-09 may 2000 dynamic conditional correlation – a simple class of multivariate garch models Robert F . Engle July 1999 Revised May 2000. https://doi.org/10.2139/ssrn.236998 DOI: https://doi.org/10.2139/ssrn.236998
Engle, R. F. and Sheppard, K. (2001). Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH. NBER Working, Paper No. 8554. https://doi.org/10.3386/w8554 DOI: https://doi.org/10.3386/w8554
Engle, R. F., & Patton, A. J. (2001). What good is a volatility model? Quantitative Finance. Institute of Physics Publishing, 1, 237-245. https://doi.org/10.1088/1469-7688/1/2/305 DOI: https://doi.org/10.1088/1469-7688/1/2/305
Evans, T., & McMillan, D. G. (2009). Financial co-movement and correlation: Evidence from 33 international stock market indices. International Journal of Banking, Accounting and Finance, 1(3), 215–241. https://doi.org/10.1504/IJBAAF.2009.022711 DOI: https://doi.org/10.1504/IJBAAF.2009.022711
Ewing, B. T., & Seyfried, W. (2003). Modeling The Philips Curve: A Time-Varying Volatility Approach. Applied Econometrics and International Development, 3(2).
Fan, J., Wang, M., & Yao, Q. (2008). Modelling multivariate volatilities via conditionally uncorrelated components. Journal of the Royal Statistical Society: series B (statistical methodology), 70(4), 679-702. https://doi.org/10.1111/j.1467-9868.2008.00654.x DOI: https://doi.org/10.1111/j.1467-9868.2008.00654.x
Ferreira, P. (2017). Portuguese and Brazilian stock market integration: a non-linear and detrended approach. Portuguese Economic Journal, 16(1), 49-63. https://doi.org/10.1007/s10258-017-0127-z DOI: https://doi.org/10.1007/s10258-017-0127-z
Fildes, R., & Petropoulos, F. (2015). Simple versus complex selection rules for forecasting many time series. Journal of Business Research, 68(8), 1692-1701. https://doi.org/10.1016/j.jbusres.2015.03.028 DOI: https://doi.org/10.1016/j.jbusres.2015.03.028
Ghufran, B., Awan, H. M., Khakwani, A. K., & Qureshi, M. A. (2016). What causes stock market volatility in Pakistan? Evidence from the field. Economics Research International, pg-9. https://doi.org/10.1155/2 016/3698297 DOI: https://doi.org/10.1155/2016/3698297
Gkillas, K., Tsagkanos, A., & Vortelinos, D. I. (2019). Integration and risk contagion in financial crises: Evidence from international stock markets. Journal of Business Research, 104, 350-365. https://doi.org/10.101 6/j.jbusres.2019.07.031 DOI: https://doi.org/10.1016/j.jbusres.2019.07.031
Hanif, M. (2020). Relationship between oil and stock markets: Evidence from Pakistan stock exchange. International Journal of Energy Economics and Policy, 10(5), 150. https://doi.org/10.324 79/ijeep.9653 DOI: https://doi.org/10.32479/ijeep.9653
Herbert, W. E., Ugwuanyi, G. O., & Nwaocha, E. I. (2019). Volatility clustering, leverage effects and risk-return trade-off in the Nigerian stock market. Journal of Finance and Economics, 7(1), 1-13.
Hkiri, B., Hammoudeh, S., Aloui, C., & Shahbaz, M. (2018). The interconnections between US financial CDS spread and control variables: New evidence using partial and multivariate wavelet coherences. International Review of Economics & Finance, 57, 237-257. https://doi.org/10.1016/j.iref.2018.01.011 DOI: https://doi.org/10.1016/j.iref.2018.01.011
Iqbal, J. (2014). Stock Market in Pakistan : An Overview Stock Market in Pakistan : An Overview Stock Market in Pakistan : An Overview By Javed Iqbal Department of Econometrics and Business Statistics , Monash Department of Statistics, University of Karachi, Pakistan Postal Address: Department of Statistics, Karachi University, Karachi 75270, Pakistan. May. https://doi.org/10.1177/097265271101100103 DOI: https://doi.org/10.1177/097265271101100103
Ismail, M. T., Audu, B., & Tumala, M. M. (2016). Comparison of forecasting performance between MODWT-GARCH (1, 1) and MODWT-EGARCH (1, 1) models: Evidence from African stock markets. The Journal of Finance and Data Science, 2(4), 254-264. https://doi.org/10.1016/j.jfds.2017.03.001 DOI: https://doi.org/10.1016/j.jfds.2017.03.001
Iyiegbuniwe, W., Ezike, J. E., & Amah, P. N. (2012). Heteroskedasticity of Market Return : A Look at the All Nigerian Stock Exchange Index Time Series. 7(16), 13–30. https://doi.org/10.5539/ijbm.v7n16p13 DOI: https://doi.org/10.5539/ijbm.v7n16p13
Jiang, Y., Yu, M., & Hashmi, S. M. (2017). The financial crisis and co-movement of global stock markets—A case of six major economies. Sustainability, 9(2), 260. https://doi.org/10.3390/su9020260 DOI: https://doi.org/10.3390/su9020260
Joyo, A. S. (2019). Stock Market Integration of Pakistan with Its Trading Partners : A Multivariate DCC-GARCH Model Approach. https://doi.org/10.3390/su11020303 DOI: https://doi.org/10.3390/su11020303
Khan, M. I., Akhter, W., & Bhutta, U. (2020). Interest Rate Exposure and Stocks Returns during Global Financial Crisis: Evidence from Islamic and Conventional Markets. Journal of Islamic Business and Management, 10(1), 131-147. https://doi.org/10.26501/jibm/2020.1001-009 DOI: https://doi.org/10.26501/jibm/2020.1001-009
Khan, M. N., Fifield, S. G., Tantisantiwong, N., & Power, D. M. (2021). Changes in co-movement and risk transmission between South Asian stock markets amidst the development of regional cooperation. Financial Markets and Portfolio Management, 1-31. https://doi.org/10.1007/s11408-021-00386-4 DOI: https://doi.org/10.1007/s11408-021-00386-4
Kizys, R., & Pierdzioch, C. (2009). Changes in the international comovement of stock returns and asymmetric macroeconomic shocks. Journal of International Financial Markets, Institutions and Money, 19(2), 289–305. https://doi.org/10.1016/j.intfin.2008.01.002 DOI: https://doi.org/10.1016/j.intfin.2008.01.002
Lal, S. (2019). Determinants of Integration of Stock Markets: A Study of Pakistan and its Trading Partners (Doctoral dissertation, CAPITAL UNIVERSITY).
Lim, S. J., & Masih, M. (2017). Exploring portfolio diversification opportunities in Islamic capital markets through bitcoin: evidence from MGARCH-DCC and Wavelet approaches. Munich Personal RePEc Archive, MPRA Paper 2017. Available online: https://mpra.ub.uni-muenchen.de/id/eprint/79752 (accessed on 10 August 2018).
Nasreen, S., Asif, S., Naqvi, A., Tiwari, A. K., & Hammoudeh, S. (2020). A Wavelet-Based Analysis of the Co-Movement between Sukuk Bonds and Shariah Stock Indices in the GCC Region: Implications for Risk Diversification. Gifr 2019, 1–22. DOI: https://doi.org/10.3390/jrfm13040063
Nguyen, T. H. T. B., & Lam, A. H. (2017). Financial development, international trade, and stock market integration: Evidence in six southeastern Asia countries. Journal of Economics and Development, 19(3), 5-17. https://doi.org/10.33301/JED.2017.19.03.01 DOI: https://doi.org/10.33301/JED.2017.19.03.01
Panda, A. K., & Nanda, S. (2018). Time-varying synchronization and dynamic conditional correlation among the stock market returns of leading South American economies. International Journal of Managerial Finance. https://doi.org/10.1108/IJMF-11-2016-0206 DOI: https://doi.org/10.1108/IJMF-11-2016-0206
Paramati, S. R., Roca, E., & Gupta, R. (2016). Economic integration and stock market dynamic linkages: evidence in the context of Australia and Asia. Applied Economics, 48(44), 4210-4226. https://doi.org/10.1080/00036846.2016.1153794 DOI: https://doi.org/10.1080/00036846.2016.1153794
Paramati, S. R., Zakari, A., Jalle, M., Kale, S., & Begari, P. (2018). The dynamic impact of bilateral trade linkages on stock market correlations of Australia and China. Applied Economics Letters, 25(3), 141-145. https://doi.org/10.1080/13504851.2017.1305067 DOI: https://doi.org/10.1080/13504851.2017.1305067
Piljak, V. (2013). Essays on the co-movement dynamics of frontier/emerging and developed financial markets. Printed with kind permission of John Wiley and Sons.
Pirzado, A. A., Jatoi, I. K., Pirzado, M. B., Khaskali, S. A., Chohan, R., & Phulpoto, S. N. (2020). An econometric analysis among different markets of pakistan using dcc-garch model. PalArch's Journal of Archaeology of Egypt/Egyptology, 17(7), 16352-16359.
Rangel, J. G., & Engle, R. F. (2012). The Factor–Spline–GARCH model for high and low-frequency correlations. Journal of Business & Economic Statistics, 30(1), 109-124. https://doi.org/10.1080 /07350015.2012.643132 DOI: https://doi.org/10.1080/07350015.2012.643132
Sakti, M. R. P., Masih, M., Saiti, B., & Tareq, M. A. (2018). Unveiling the diversification benefits of Islamic equities and commodities. Managerial Finance, 44(6), 830-850. https://doi.org/10.1108/MF-08-2017-0278 DOI: https://doi.org/10.1108/MF-08-2017-0278
Savva, C. S. (2009). International stock markets interactions and conditional correlations. Journal of International Financial Markets, Institutions and Money, 19(4), 645-661. https://doi.org/10.1016/ j.intfin.2008.11.001 DOI: https://doi.org/10.1016/j.intfin.2008.11.001
Swiler, L., Urbina, A., & Williams, B. (2011, March). Multiple Model Inference: Calibration, Selection, and Prediction with Multiple Models. In 52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 19th AIAA/ASME/AHS Adaptive Structures Conference 13t (p. 1844). https://doi.org/10.2514/6.2011-1844 DOI: https://doi.org/10.2514/6.2011-1844
Teng, L., Ehrhardt, M., & Günther, M. (2016). The dynamic correlation model and its application to the Heston model. Springer Proceedings in Mathematics and Statistics, 165(November), 437–449. https://doi.org/10.100 7/978-3-319-33446-2_21 DOI: https://doi.org/10.1007/978-3-319-33446-2_21
Trivedi, J., Spulbar, C., Birau, R., & Mehdiabadi, A. (2021). Modelling volatility spillovers, cross-market correlation and co-movements between stock markets in European Union: an empirical case study. Business, Management and Economics Engineering, 19(1), 70-90. https://doi.org/10.3846/bmee.2021.13588 DOI: https://doi.org/10.3846/bmee.2021.13588
Uddin, G. S. (2013). Co-movements between Germany and International Stock Markets: Some New Evidence from DCC-GARCH and Wavelet Approaches. 1–23. Available at SSRN 2311724. ttps://doi.org/10.2139/ ssrn.2311724 DOI: https://doi.org/10.2139/ssrn.2311724
Vacha, L., & Barunik, J. (2012). Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis. Energy Economics, 34(1), 241–247. https://doi.org/10.1016/j.eneco.2011.10.007 DOI: https://doi.org/10.1016/j.eneco.2011.10.007