بررسی سرریز ریسک پویا نامتقارن در بازار فلزات اساسی: شواهدی از مدیریت مواد مصرفی مجتمع صنایع مس شهید باهنر

Roudari, Soheil and Ahmadian- Yazdi, Farzaneh and Namazizadeh, Ehsan (2024): بررسی سرریز ریسک پویا نامتقارن در بازار فلزات اساسی: شواهدی از مدیریت مواد مصرفی مجتمع صنایع مس شهید باهنر.

Abstract

Industrial metals, particularly copper, zinc, lead, and nickel, play a pivotal role not only in determining the pricing of manufactured goods but also in ensuring the economic security of nations. Analyzing the return interconnections of these metals under varying market conditions is crucial. This study employs the Time-Varying Parameter Vector Autoregression (TVP-VAR) model over the period from January 1990 to May 2024 to examine the price fluctuations of these metals, which are essential inputs for Shahid Bahonar Copper Industries. The results indicate that zinc and lead are the primary risk transmitters in normal and bullish markets, whereas in bearish markets, zinc acts as the main transmitter and nickel as the primary receiver of volatility. Additionally, significant asymmetries in volatility transmission are observed between these metals during bullish and bearish conditions, with more pronounced effects during events such as the 2008 financial crisis (for copper and zinc) and the Russia-Ukraine war (for zinc and lead). This research also offers practical insights for industry managers, enabling them to determine optimal timing for purchasing raw materials and selling products based on the co-movement or divergence of metal prices. The proposed strategies focus on minimizing costs and maximizing profitability, providing a comprehensive framework for effectively managing volatility in metal markets.

• Item Type: MPRA Paper
• English Title: Examining Asymmetric Dynamic Risk Spillover in the Base Metals Market: Evidence from Material Management at Shahid Bahonar Copper Industries Complex
• Language: Persian
• Keywords: Industrial metal, Risk management, Bullish and Bearish Markets, Cost management, Asymmetric TVP-VAR Model
• Subjects: E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E44 - Financial Markets and the Macroeconomy; G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading; G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill

References

Adekoya, O. B., & Oliyide, J. A. (2020). The hedging effectiveness of industrial metals against different oil shocks: Evidence from the four newly developed oil shocks datasets. Resources Policy, 69, Article 101831.

Ahmadian-Yazdi, F., Roudari, S., Omidi, V., Mensi, W., & Al-Yahyaee, K. H. (2024). Contagion effect between fuel fossil energies and agricultural commodity markets and portfolio management implications. International Review of Economics & Finance, 95, 103492.

Ahmed, A, Huo, R. (2021). Volatility transmissions across international oil market, commodity futures and stock markets: Empirical evidence from China, Energy Economics, 93,1-14.

Alshater, M. M., Alqaralleh, H., & El Khoury, R. (2023). Dynamic asymmetric connectedness in technological sectors. The Journal of Economic Asymmetries, 27, 1-15.

Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84.

Aroury, M.E.H. Lahiani, A. &khuong Nguyan D. (2015). World gold prices and stock returns in China: Insights for hedging and diversification strategies. Economic Modeling, 44, 273-282.

Cao, G., & Xie, W. (2022). Asymmetric dynamic spillover effect between cryptocurrency and China's financial market: Evidence from TVP-VAR based connectedness approach. Finance Research Letters, 49, 103070.

Cheng, S., Deng, M., Liang, R., & Cao, Y. (2023). Asymmetric volatility spillover among global oil, gold, and Chinese sectors in the presence of major emergencies. Resources Policy, 82, 103579.

Dutta, A. (2018). Impacts of oil volatility shocks on metal markets: A research note. Resources Policy, 55, 9-19.

Fan, Z. (2024). The risk spillover relationship between major agricultural commodities in China and the US. In Proceedings of the 4th International Conference on Economic Development and Business Culture (ICEDBC 2024) (pp. 221-233). Atlantis Press.

Gkillas, K., Vortelinos, D. I., & Suleman, T. (2018). Asymmetries in the African financial markets. Journal of Multinational Financial Management, 45, 72-87.

Gong, X., & Xu, J. (2022). Geopolitical risk and dynamic connectedness between commodity markets. Energy Economics, 110, Article 106028.

Gong, X., & Lin, B. (2021). Effects of structural changes on the prediction of downside volatility in futures markets. Journal of Futures Markets, 41(7), 1124-1153.

Han, X., Liu, Z., & Wang, S. (2021). An R-vine copula analysis of non-ferrous metal futures with application in Value-at-Risk forecasting. Journal of Commodity Markets, 100188.

Jiang,Y., Fu,Y., Ruan,W. (2019) Risk spillovers and portfolio management between precious metal and BRICS stock markets. Physica A, 534,120993.

Karolyi, G. A., & Stulz, R. M. (1996). Why do markets move together? An investigation of US‐Japan stock return comovements. The Journal of Finance, 51(3), 951-986.

Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of econometrics, 74(1), 119-147.

Li, X., Li, B., Wei, G., Bai, L., Wei, Y., & Liang, C. (2021). Return connectedness among commodity and financial assets during the COVID-19 pandemic: Evidence from China and the US. Resources Policy, 73, 102166.

Liew, P. X., Lim, K. P., & Goh, K. L. (2022). The dynamics and determinants of liquidity connectedness across financial asset markets. International Review of Economics & Finance, 77, 341-358.

Mensi, W., Ahmadian-Yazdi, F., Al-Kharusi, S., Roudari, S., & Kang, S. H. (2024). Extreme Connectedness Across Chinese Stock and Commodity Futures Markets. Research in International Business and Finance, 70, 102299.

Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics letters, 58(1), 17-29.

Reboredo, J. C., Ugolini, A., & Hernandez, J. A. (2021). Dynamic spillovers and network structure among commodity, currency, and stock markets. Resources Policy, 74, 102266.

Rehman, M. U., Vo, X. V., Ko, H. U., Ahmad, N., & Kang, S. H. (2023). Quantile connectedness between Chinese stock and commodity futures markets. Research in International Business and Finance, 64, 101810.

Saiti, B., Bacha, O. I., & Masih, M. (2016). Testing the conventional and Islamic financial market contagion: evidence from wavelet analysis. Emerging Markets Finance and Trade, 52(8), 1832-1849.

Salisu, A, Isah, K, A, A (2019). Dynamic spillovers between stock and money markets in Nigeria: A VARMA-GARCH approach. Review of Economic Analysis ,11,255-283.

Tang, Y., Xiao, X., Wahab, M. I. M., & Ma, F. (2021). The role of oil futures intraday information on predicting US stock market volatility. Journal of Management Science and Engineering, 6, 64–74.

Yunus, N. (2020). Time-varying linkages among gold, stocks, bonds and real estate. The Quarterly Review of Economics and Finance, 77, 165-185.