بررسی ارتباط بین گروه بانکها، خودرو، سیمان، فلزات اساسی و فرآورده های نفتی در بورس اوراق بهادار تهران به تفکیک شرایط با بازدهی مثبت و منفی با استفاده از الگوی Asymmetric TVP-VAR

Omidi, Vahid and Roudari, Soheil and Jamshidi, Amir (2023): بررسی ارتباط بین گروه بانکها، خودرو، سیمان، فلزات اساسی و فرآورده های نفتی در بورس اوراق بهادار تهران به تفکیک شرایط با بازدهی مثبت و منفی با استفاده از الگوی Asymmetric TVP-VAR. Published in: Journal of Financial Management Strategy , Vol. 12, No. 44 (10 May 2024): pp. 69-86.

Abstract

The interplay between various industrial groups plays a crucial role in determining the optimal investment portfolio for investors. Identifying which group carries or accepts risk within a specific time period and performance range allows for necessary adjustments in the investor's portfolio to achieve maximum returns. In this regard, the present study examines the impact of banking, automotive, cement, basic metals, and petroleum products groups on a symmetric, positive, and negative performance basis from January 5, 2015, to February 17, 2023. The results of the study indicate that in recent years, the overall index of these mentioned groups has shown more negative performance than positive performance. Moreover, banks and basic metals have acted as guiding and risk-transferring entities to other groups. On the other hand, the automotive and petroleum products groups have been risk-accepting, and their performance can be explained by the two groups of banks and basic metals.

• Item Type: MPRA Paper
• English Title: Investigating The Relationship Between Bank, Automotive, Cement, Base Metals, And Petroleum Products in Tehran Stock Exchange in Positive and Negative Return by Asymmetric TVP-VAR
• Language: Persian
• Keywords: Asymmetric TVP-VAR, Portfolio, Return, Base Metals, Bank, Tehran Stock Exchange
• Subjects: G - Financial Economics > G0 - General > G01 - Financial Crises; G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation; 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., Akinseye, A. B., Antonakakis, N., Chatziantoniou, I., Gabauer, D., & Oliyide, J. (2022). Crude oil and Islamic sectoral stocks: Asymmetric TVP-VAR connectedness and investment strategies. Resources Policy, 78, 102877.

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, e00287.

Antonakakis, N., Chatziantoniou, I., and 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.

Frankel, J. A. (1992). Monetary and portfolio-balance models of exchange rate determination. In International economic policies and their theoretical foundations (pp. 793-832). Academic Press.

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

Karolyi, G. A. (1995). A multivariate GARCH model of international transmissions of stock returns and volatility: The case of the United States and Canada. Journal of Business & Economic Statistics, 13(1), 11-25.

Koop, G., Pesaran, M. H., and 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.

Pesaran, H. H. and Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1):17–29.

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.

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

Saiti, B., & Masih, M. (2016). The co-movement of selective conventional and Islamic stock indices: is there any impact on shariah compliant equity investment in China? International Journal of Economics and Financial Issues, 6(4), 1895-1905.

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