Dynamic Linkages among Stocks and Commodities: A Switching Copula Approach

  • Hina Munir Abbasi Comsats University, Islamabad, Pakistan
  • Naveed Raza Comsats University, Islamabad, Pakistan
Keywords: Commodities, Islamic stock, Conventional stock, Copula, Diversification

Abstract

The fluctuating behavior of financial markets has significant impact on economic variables. A relatively new modeling technique, “switching dependence copula” is employed to characterize conditional dependence among stock indices (Islamic/conventional stocks) and commodities. As the dependence may switch in between negative and positive correlation regimes with the passage of time where the copula captures dependence structure more conveniently and portrays pictures most relevantly then a single copula regime. The sample period ranges from 2011 to 2021. All the data sourced from Thomson Reuters. Overall, the results are in favor of commodities phenomenon of providing better hedging and diversification benefit to stock indices. Fluctuating behavior is observed for Islamic stocks and commodities pairs. According to the results, commodities are suitable as cousin during crisis period, especially during negative correlation regime; commodities perform better, providing hedging and diversification benefits.

References

Albulescu, C. T., Tiwari, A. K., & Ji, Q. (2020). Copula-based local dependence among energy, agriculture and metal commodities markets. Energy, 202, 1-22. https://doi.org/10.1016/j.energy.2020.117762.

Azimli, A. (2022). Degree and structure of return dependence among commodities, energy stocks and international equity markets during the post-COVID-19 period. Resources Policy, 77. https://doi.org/10.1016/j.resourpol.2022.102679.

Abedifar, P., Molyneux, P., & Tarazi, A. (2013). Risk in Islamic banking. Review of finance, 17(6). https://doi.org/10.1093/rof/rfs041.

Aloui, C., & Jammazi, R. (2009). The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach. Energy Economics, 31(5), 789-799. https://doi.org/10.1016/j.eneco.2009.03.009.

Arouri, M. E. H., Jouini, J., & Nguyen, D. K. (2011). Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management. Journal of International money and finance, 30(7), 1387-1405. https://doi.org/10.1016/j.jimonfin.2011.07.008.

Baker, H. K., Kumar, S., Goyal, K., & Sharma, A. (2021). International review of financial analysis: A retrospective evaluation between 1992 and 2020. International Review of Financial Analysis, 78, 1-26. https://doi.org/10.1016/j.irfa.2021.101946.

Bossman, A., & Agyei, S. K. (2022). Interdependence structure of global commodity classes and African equity markets: A vector wavelet coherence analysis. Resources Policy, 79, 1-39. https://doi.org/10.1016/j.resourpol.2022.103039.

Bü yü kşahin, B., & Robe, M. A. (2014). Speculators, commodities and cross-market linkages. Journal of International Money and Finance, 42, 38-70. https://doi.org/10.1016/j.jimonfin.2013.08.004.

Chollete, L., Heinen, A., & Valdesogo, A. (2009). Modeling international financial returns with a multivariate regime-switching copula. Journal of Financial Econometrics, 7(4), 437-480. https://doi.org/10.1093/jjfinec/nbp014.

Oztek, M. F., & Ocal, N. (2017). Financial crises and the nature of correlation between commodity and stock markets. International Review of Economics and Finance, 48, 56-68. https://doi.org/10.1016/j.iref.2016.11.008.

Chkili, W. (2016). Dynamic correlations and hedging effectiveness between gold and stock markets: Evidence for BRICS countries. Research in International Business and Finance, 38, 22-34. https://doi.org/10.1016/j.ribaf.2016.03.005.

Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial econometrics, 4(4), 537-572. https://doi.org/10.1093/jjfinec/nbl005.

Chang, C. L., McAleer, M., & Wang, Y. A. (2020). Herding behaviour in energy stock markets during the global financial crisis, SARS, and ongoing COVID-19. Renewable and Sustainable Energy Reviews, 134, 1-19. https://doi.org/10.1016/j.rser.2020.110349.

Chang, C. L., McAleer, M., & Wong, W. K. (2020). Risk and financial management of COVID-19 in business, economics and finance. Journal of Risk and Financial Management, 13(5), 102. https://doi.org/10.3390/jrfm13050102.

Chang, C.L., McAleer, M., Tansuchat, R., (2013). Conditional correlations and volatility spillovers between crude oil and stock index returns. North American Journal of Economics and Finance. 25, 116–138. https://doi.org/10.1016/j.najef.2012.06.002.

Charles, A., Darné, O., & Pop, A. (2015). Risk and ethical investment: Empirical evidence from Dow Jones Islamic indexes. Research in International Business and Finance, 35, 33-56.https://doi.org/10.1016/j.ribaf.2015.03.003.

Choi, K., & Hammoudeh, S. (2010). Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment. Energy policy, 38(8), 4388-4399. https://doi.org/10.1016/j.enpol.2010.03.067.

Daskalaki, C., Skiadopoulos, G., & Topaloglou, N. (2017). Diversification benefits of commodities: A stochastic dominance efficiency approach. Journal of Empirical Finance, 44, 250-269. https://doi.org/10.1016/j.jempfin.2017.07.004.

Du, X. (2013). Does religion matter to owner-manager agency costs? Evidence from China. Journal of Business Ethics, 118(2), 319-347. https://doi.org/10.1007/s10551-012-1569-y.

Erb, C. B., & Harvey, C. R. (2016). Conquering mis perceptions about commodity futures investing. Financial Analysts Journal, 72(4), 26-35. https://doi.org/10.2469/faj.v72.n4.3.

Hammoudeh, S., Mensi, W., Reboredo, J. C., & Nguyen, D. K. (2014). Dynamic dependence of the global Islamic equity index with global conventional equity market indices and risk factors. Pacific-Basin Finance Journal, 30, 189-206. https://doi.org/10.1016/j.pacfin.2014.10.001.

Harrathi, N., Aloui, C., Houfi, M. A., & Majdoub, J. (2016). Emerging equity markets connectedness, portfolio hedging strategies and effectiveness. International Journal of Financial Research, 7(2), 189-201. http://dx.doi.org/10.5430/ijfr.v7n2p189.

Hao, P. Y., Kung, C. F., Chang, C. Y., & Ou, J. B. (2021). Predicting stock price trends based on financial news articles and using a novel twin support vector machine with fuzzy hyperplane. Applied Soft Computing, 98, 1-16. https://doi.org/10.1016/j.asoc.2020.106806.

Henriques, I., & Sadorsky, P. (2018). Investor implications of divesting from fossil fuels. Global Finance Journal, 38, 30-44. https://doi.org/10.1016/j.gfj.2017.10.004.

Ho, C. S. F., Abd Rahman, N. A., Yusuf, N. H. M., & Zamzamin, Z. (2014). Performance of global Islamic versus conventional share indices: International evidence. Pacific-Basin Finance Journal, 28, 110-121. https://doi.org/10.1016/j.pacfin.2013.09.002.

Jawad, H.S. Bouri,E. Roubaud,D. Ladislav,K. (2019). Safe haven, hedge and diversification for G7 stock markets: Gold versus bitcoin. Journal of Economic Development, 21(3) 7-23. https://doi.org/10.1016/j.econmod.2019.07.023.

Ji, Q., Bouri, E., Roubaud, D., & Shahzad, S. J. H. (2018). Risk spillover between energy and agricultural commodity markets: A dependenceswitching CoVaR-copula model. Energy Economics, 75, 14-27. https://doi.org/10.1016/j.eneco.2018.08.015.

Joe H.(2015). Dependence modeling with copulas. Florida, FL: CRC Press.

Singh, J., Ahmad, W., & Mishra, A. (2019). Coherence, connectedness and dynamic hedging effectiveness between emerging markets equities and commodity index funds. Resources Policy, 61, 441-460. https://doi.org/10.1016/j.resourpol.2018.03.006.

Khalfaoui, R., Baumö hl, E., Sarwar, S., & Výrost, T. (2021). Connectedness between energy and non-energy commodity markets: evidence from quantile coherency networks. Resources Policy, 74, 1-18. https://doi.org/10.1016/j.resourpol.2021.102318.

Kroner, K. F., & Ng, V. K. (1998). Modeling asymmetric comovements of asset returns. The review of financial studies, 11(4), 817-844. https://doi.org/10.1093/rfs/11.4.817.

Lai, Y. S. (2018). Dynamic hedging with futures: A copula-based GARCH model with high-frequency data. Review of Derivatives Research, 21(3), 307-329.https://doi.org/10.1007/s11147-018-9142-1.

Lee, C. C., Yuan, Z., & Ho, S. J. (2022). How does export diversification affect income inequality? International evidence. Structural Change and Economic Dynamics, 63, 410-421. https://doi.org/10.1016/j.strueco.2022.06.010.

Li, Y. F. (1999). Global technical hexachlorocyclohexane usage and its contamination consequences in the environment: from 1948 to 1997. Science of the total environment, 232(3), 121-158. https://doi.org/10.1016/S0048-9697(99)00114-X.

Maghyereh, A., Awartani, B., & Hassan, A. (2018). Can gold be used as a hedge against the risks of Sharia-compliant securities? Application for Islamic portfolio management. Journal of Asset Management, 19(6), 394-412. https://doi.org/10.1057/s41260-018-0090-y.

Maghyereh, A. I., Abdoh, H., & Awartani, B. (2019). Connectedness and hedging between gold and Islamic securities: A new evidence from time-frequency domain approaches. Pacific-Basin Finance Journal, 54, 13-28. https://doi.org/10.1016/j.pacfin.2019.01.008.

Markowitz, H., 1952. Portfolio selection. Journal of Finance. 7 (1), 77–91.

Oztek, M. F., & Ocal, N. (2017). Financial crises and the nature of correlation between commodity and stock markets. International Review of Economics and Finance, 48, 56-68. https://doi.org/10.1016/j.iref.2016.11.008.

Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International economic review, 47(2), 527-556. https://doi.org/10.1111/j.1468-2354.2006.00387.x.

Raza, N., Ali, S., Shahzad, S. J. H., Rehman, M. U., & Salman, A. (2019). Can alternative hedging assets add value to Islamic-conventional portfolio mix: Evidence from MGARCH models. Resources Policy, 61, 210-230. https://doi.org/10.1016/j.resourpol.2019.02.013.

Reboredo, J. C. (2012). Modelling oil price and exchange rate co-movements. Journal of Policy Modeling, 34(3), 419-440. https://doi.org/10.1016/j.jpolmod.2011.10.005.

Reboredo, J. C., Rivera-Castro, M. A., & Ugolini, A. (2017). Wavelet-based test of co-movement and causality between oil and renewable energy stock prices. Energy Economics, 61, 241-252. https://doi.org/10.1016/j.eneco.2016.10.015.

Rizvi, S. A. R., Arshad, S., & Alam, N. (2015). Crises and contagion in Asia Pacific—Islamic v/s conventional markets. Pacific-Basin Finance Journal, 34, 315-326. https://doi.org/10.1016/j.pacfin.2015.04.002.

Sklar, A. (1996). Random variables, distribution functions, and copulas: A personal look backward and forward. Monograph Series, 281-14.

Su, C. W., Wang, X. Q., Tao, R., & Oana-Ramona, L. (2019). Do oil prices drive agricultural commodity prices? Further evidence in a global bio-energy context. Energy, 172, 691-701. https://doi.org/10.1016/j.energy.2019.02.028.

Tiwari, A. K., Khalfaoui, R., Solarin, S. A., & Shahbaz, M. (2018). Analyzing the time-frequency lead–lag relationship between oil and agricultural commodities. Energy Economics, 76, 470-494. https://doi.org/10.1016/j.eneco.2018.10.037.

Wajahat, A. Adam,N. Ginanjar, D. Ruslan, N. (2019). Doing well while doing good: The case of Islamic and sustainability equity investing. Borsa Istanbul Review, 19(3), 207-218. https://doi.org/10.1016/j.bir.2019.02.002.

Wang, Y. M., Lin, C. F., & Li, Y. H. (2013). The correlation and hedging effects between commodity and stock markets. Journal of Applied Finance and Banking, 3(5), 269-281.

Published
2022-11-25
How to Cite
Hina Munir Abbasi, & Naveed Raza. (2022). Dynamic Linkages among Stocks and Commodities: A Switching Copula Approach. Journal of Management Practices, Humanities and Social Sciences, 6(6), 35-61. https://doi.org/10.33152/jmphss-6.6.5
Section
Articles