Research on World Agricultural Economy

The Relationship between the Wheat Market and the Financial Market in Malaysia Using a Dynamic Conditional Correlation Model (DCC-GARCH)

Wisam H. Ali Al-Anezi

Department of Economics, College of Administration and Economics, University of Anbar, Ramadi 31001, Iraq

Ali Y. Abdullah Al-Joaani

Ministry of Finance, Ramadi 31001, Iraq

Abdulrazaq Shabeeb

Department of Economics, College of Administration and Economics, University of Anbar, Ramadi 31001, Iraq

Faez Hlail Srayyih

Department of Economics, College of Administration and Economics, University of Anbar, Ramadi 31001, Iraq

Faisal Ghazi Faisal

Department of Finance and Banking , Al‑Idrisi University College, Ramadi 31001, Iraq

DOI: https://doi.org/10.36956/rwae.v6i1.1480

Received: 15 November 2024 | Revised: 2 December 2024 | Accepted: 5 December 2024 | Published Online: 6 February 2025

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Abstract

The research aims to identify the nature of the dynamic relationship between the returns of the wheat market (RPW) and the returns of the general index of the Malaysia financial market (RISX), and to verify the transmission of shocks and volatility between the returns of both markets during the monthly period from (1/1/2004) to (31/10/2024). Using the Dynamic Conditional Correlation GARCH (DCC-GARCH) model, the results showed an inverse relationship between the returns of both markets, with the correlation sensitivity reaching (–0.08%). This indicates a weak ratio, suggesting that diversifying the portfolio in both markets may increase gains to some extent. The predictive results indicated a positive correlation, which is also weak. As it refers to sensitivity to returns in both markets for dynamic changes that occur over time, there may not be an opportunity to achieve diversification when prices are imbalanced and to achieve returns in these markets. Therefore, the weak correlation between the returns of both markets indicates a decrease in their integration. The research recommended the need to increase the openness of the Malaysian securities market and to enhance its informational efficiency and transparency to increase the capacity and smoothness of the flow of information to and from the financial market.

Keywords: Dynamic Correlations; DCC-GARCH; Portfolio Diversification; Investor Interest

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References

[1] Pandey, D., Verma, N., Islam, T., et al., 2021. The response of Consumer Food Price Index (CFPI) due to the impact of pandemic COVID-19 on indian agriculture sector. Journal of Agricultural Sciences. 3(1), 29–35. DOI: https://doi.org/10.36956/njas.v3i1.370

[2] Akbas, F., Armstrong, W.J., Sorescu, S., et al., 2015. Smart money, dumb money, and capital market anomalies. Journal of Financial Economics. 118(2), 355–382. DOI: https://doi.org/10.1016/j.jfineco.2015.07.003

[3] Azar, S.A., Chopurian, N.A., 2018. Commodity indexes and the stock markets of the GCC countries. Arab Economic and Business Journal. 13(2), 134–142. DOI: https://doi.org/10.1016/j.aebj.2018.08.001

[4] Khan, A., Masih, M., 2014. Correlation between Islamic stock and commodity markets: An investigation into the impact of financial crisis and financialization of commodity markets; MPRA Paper no. 56979, 29 June 2014. Available from: https://mpra.ub.uni-muenchen.de/56979/1/MPRA_paper_56979.pdf

[5] Hammad, S.A., Outhman, N.Y., Hamad, M.F., et al., 2024. The impact of domestic public debt on agricultural government investment and economic growth: Evidence from Iraqi between 2004 and 2022. Research World Agricultural Economy. 5(3), 37–50. DOI: https://doi.org/10.36956/rwae.v5i3.1124

[6] Kalhory, D.A., Hasan, T.M.L., Sakinaa, M.O., 2024. The attitude of full-time extension workers towards supervising the work of private agricultural projects in Sulaymani Governorate. Anbar Journal of Agricultural Sciences. 22(1), 28–40, DOI: https://doi.org/10.32649/ajas.2024.183694

[7] Creti, A., Joëts, M., Mignon, V., 2013. On the links between stock and commodity markets' volatility. Energy Economics. 37, 16–28, DOI: https://doi.org/10.1016/j.eneco.2013.01.005

[8] Ji, Q., Fan, Y., 2012. How does oil price volatility affect non-energy commodity markets?. Applied Energy. 89(1), 273–280. DOI: https://doi.org/10.1016/j.apenergy.2011.07.038

[9] Bai, Y., Green, C.J., 2010. International diversification strategies: Revisited from the risk perspective. Journal of Banking and Finance. 34(1), 236–245, DOI: https://doi.org/10.1016/j.jbankfin.2009.07.026

[10] Balli, F., Naeem, M.A., Shahzad, S.J.H., et al., 2019. Spillover network of commodity uncertainties. Energy Economics. 81, 914–927. DOI: https://doi.org/10.1016/j.eneco.2019.06.001

[11] Mensi, W., Beljid, M., Boubaker, A., et al., 2013. Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold. Economic Modelling. 32(1), 15–22. DOI: https://doi.org/10.1016/j.econmod.2013.01.023

[12] Kang, S.H., McIver, R., Yoon, S.M., 2017. Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets. Energy Economics. 62, 19–32. DOI: https://doi.org/10.1016/j.eneco.2016.12.011

[13] Sadorsky, P., 2014. Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat. Energy Economics. 43, 72–81. DOI: https://doi.org/10.1016/j.eneco.2014.02.014

[14] Gozgor, G., Lau, C.K.M., Bilgin, M.H., 2016. Commodity markets volatility transmission: Roles of risk perceptions and uncertainty in financial markets. Journal of International Financial Markets, Institutions and Money. 44, 35–45. DOI: https://doi.org/10.1016/j.intfin.2016.04.008

[15] Maitra, D., Guhathakurta, K., Kang, S.H., 2021. The good, the bad and the ugly relation between oil and commodities: An analysis of asymmetric volatility connectedness and portfolio implications. Energy Economics. 94, 105061. DOI: https://doi.org/10.1016/j.eneco.2020.105061

[16] Mensi, W., Al Rababa'a, A.R., Vo, X.V., et al., 2021. Asymmetric spillover and network connectedness between crude oil, gold, and Chinese sector stock markets. Energy Economics. 98, 105262. DOI: https://doi.org/10.1016/j.eneco.2021.105262

[17] Gunera, J.E.B., 2023. Cycles and dynamic correlations between agricultural and stock markets [Master's thesis]. Baton Rouge, LA, U.S.: Louisiana State University. pp. 1-59.

[18] Nordin, N., Nordin, S., Ismail, R., 2021. The impact of commodity prices, interest rate and exchange rate on stock market performance: An empirical analysis from Malaysia. Malaysian Management Journal. 18, 39–52. DOI: https://doi.org/10.32890/mmj.18.2014.9015

[19] Acatrinei, M., Gorun, A., Marcu, N., 2013. A DCC-GARCH model to estimate the risk to the capital market in Romania. Journal for Economic Forecasting. 1, 136–148.

[20] Al-Anezi, W.H.A., Mohammed, M.T., Asaad, B., 2021. The impact of financial liberalization on the value of the bank: An applied study on private Iraqi banks for the period 2011-2017. Webology. 18(3), 462–476. DOI: https://doi.org/10.14704/WEB/V18SI03/WEB18108

[21] Engle, R., 2002. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics. 20(3), 339–350. DOI: https://doi.org/10.1198/073500102288618487

[22] Bauwens, L., Laurent, S., Rombouts, J.V.K., 2006. Multivariate GARCH models: A survey. Journal of Applied Econometrics. 21(1), 79–109. DOI: https://doi.org/10.1002/jae.842

[23] Marozva, G., 2017. Africa stock markets cross-market linkages: A time-varying dynamic conditional correlations (DCC-GARCH) approach. Journal of Applied Business Research. 33(2), 321–328. DOI: https://doi.org/10.19030/jabr.v33i2.9904

[24] Amado, C., Teräsvirta, T., 2014. Conditional correlation models of autoregressive conditional heteroscedasticity with nonstationary GARCH equations. Journal of Business and Economic Statistics. 32(1), 69–87. DOI: https://doi.org/10.1080/07350015.2013.847376

[25] Al Mughairi, H., 2016. Essays on modelling the volatility dynamics and linkages of emerging and frontier stock markets [PhD thesis]. London, UK: Brunel University London. pp. 1-107.

[26] Chiang, T.C., Jeon, B.N., Li, H., 2007. Dynamic correlation analysis of financial contagion: Evidence from Asian markets. Journal of International Money and Finance. 26(7), 1206–1228. DOI: https://doi.org/10.1016/j.jimonfin.2007.06.005

[27] Kamel, A.B., Si Mohammed, 2017. Foreign exchange market contagion: Evidence of DCC and DECO Multivariate GARCH models. universite ibn khaldoun de tiaret. 14, 6–21.(in Arabic).

[28] Billio, M., Caporin, M., Gobbo, M., 2006. Flexible dynamic conditional correlation multivariate GARCH models for asset allocation. Journal of International Money and Finance. 2(2), 123–130. DOI: https://doi.org/10.1080/17446540500428843

[29] Ku, Y.H.H., Chen, H.C., Chen, K.H., 2007. On the application of the dynamic conditional correlation model in estimating optimal time-varying hedge ratios. Applied Economics Letters. 14(7), 503–509. DOI: https://doi.org/10.1080/13504850500447331

[30] Billio, M., Caporin, M., Gobbo, M., 2003. Block dynamic conditional correlation multivariate GARCH models. Working Paper No. 03.03, April 2003.

[31] Ramon, H., 2008. Forecasting the volatility of Philippine inflation using GARCH models. BSP Working Paper Series, Series No. 2008-01.