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بررسی ارتباط بین گروه بانکها، خودرو، سیمان، فلزات اساسی و فرآوردههای نفتی در بورس اوراق بهادار تهران به تفکیک شرایط با بازدهی مثبت و منفی با استفاده از الگوی Asymmetric TVP-VAR | ||
| راهبرد مدیریت مالی | ||
| مقاله 4، دوره 12، شماره 1 - شماره پیاپی 44، فروردین 1403، صفحه 69-86 اصل مقاله (502.31 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22051/jfm.2024.43995.2830 | ||
| نویسندگان | ||
| وحید امیدی* 1؛ سهیل رودری2؛ امیر جمشیدی3 | ||
| 1استادیار گروه اقتصاد، دانشکده علوم اقتصادی و اداری، دانشگاه قم، قم ایران. | ||
| 2دکتری اقتصاد، دانشکده علوم اداری و اقتصادی، دانشگاه فردوسی، مشهد، ایران | ||
| 3دانشجوی دکتری اقتصاد، دانشکده اقتصاد و علوم اجتماعی، دانشگاه شهید چمران، اهواز، ایران | ||
| چکیده | ||
| ارتباط گروههای صنعتی مختلف در تعیین سبد بهینه سرمایهگذار اهمیت زیادی دارد. اگر مشخص شود کدام گروه در چه بازه زمانی و در چه بازدهی انتقال دهنده ریسک یا پذیرنده ریسک است میتوان در پروتفوی سرمایهگذار تعدیلات لازم جهت دستیابی به بیشترین بازدهی را اعمال کرد. به این منظور در مطالعه پیش روی شیوه اثرگذاری گروههای بانکها، خودرو، سیمان، فلزات اساسی و فرآوردههای نفتی در بازه 05/01/1394 تا 17/02/1402 در حالت تقارن، بازدهی مثبت و بازدهی منفی مورد بررسی قرار گرفته است. نتیجه مطالعه بیانگر آن است که در سالهای اخیر شاخص کل ارتباط گروههای ذکر شده در بازدهی منفی بیش از بازدهی مثبت بوده است. همچنین، بانکها و فلزات اساسی نقش هدایت کننده و انتقال دهنده ریسک به سایر گروهها را داشتهاند. از سوی دیگر، گروه خودرو و فراوردههای نفتی پذیرنده ریسک بودهاند و بازدهی آنها توسط دو گروه بانکها و فلزات اساسی قابل توضیح است | ||
| کلیدواژه‌ها | ||
| Asymmetric TVP-VAR؛ پورتفو؛ بازدهی؛ بورس اوراق بهادار تهران | ||
| عنوان مقاله [English] | ||
| 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 | ||
| نویسندگان [English] | ||
| Vahid Omidi1؛ Soheil Roudari2؛ Amir Jamshidi3 | ||
| 1Assistant Professor, Department of Economics, Faculty of Economic and Administrative Sciences, University of Qom, Qom, Iran. | ||
| 2PhD in Economics, Faculty of Administrative and Economic Sciences, Ferdowsi University, Mashhad, Iran | ||
| 3PhD Student in Economics, Faculty of Economics and Social Sciences, Shahid Chamran University, Ahvaz, Iran | ||
| چکیده [English] | ||
| 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. | ||
| کلیدواژه‌ها [English] | ||
| Asymmetric TVP-VAR, Portfolio, Return, Tehran Stock Exchange | ||
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| مراجع | ||
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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.
Argha, L., Mowlaei, M., & Khezri, M. (2020). Investigating Impact of the Selected Domestic and Foreign Assets Returns on Stock Price Index Returns in Iran: An Approach from DCC-FIAPARCH Model. Quarterly Journal of Applied Theories of Economics, 6(4), 251-274. (in persian)
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.
Dadmehr, M., Rahnama Roodposhti, F., Nikoumaram, H., & Fallah Shams, M. F. (2021). Investigating the Effects of Contagion Between Monetary and Financial Markets of Iran. Journal of Economics and Modelling, 12(2), 123-166. doi: 10.29252/jem.2021.224004.1665 (in persian)
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.
hoseini, A., jahangiri, K., Heydari, H., & Ghaemi asl, M. (2019). Study of Shock and Volatility Spillovers among Selected Indices of the Tehran Stock Exchange Using Asymmetric BEKK-GARCH Model. Journal of Applied Economics Studies in Iran, 8(29), 123-155. doi: 10.22084/aes.2018.15376.2578. (in persian)
karami, sepideh, Rastegar, Mohammad Ali. (2018). Estimation of Return and Volatilities Spillover between Different Industries of Tehran Stocks’ Exchange. Financial Engineering and Portfolio Management,35,323-342. (in persian)
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.
Mohseni, H., & botshekan, M. H. (2020). Investigating Conditional correlation among Industries in the Capital Market. Budget and Finance Strategic Research, 1(1), 75-91. (in persian)
Mohajeri, P., & Taleblou, R. (2022). Investigating the Dynamics of Volatility Spillovers across Sectors’ Returns Utilizing a Time-Varying Parameter Vector Autoregressive Connectedness Approach; Evidence from Iranian Stock Market. Journal of Economic Research (Tahghighat- E- Eghtesadi), 57(2), 321-356. doi: 10.22059/jte.2023.349895.1008727 (in persian)
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.
Taleblou, R., & Mohajeri, P. (2021). Modeling the Transmission of Volatility in the Iranian Stock Market Space-State Nonlinear Approach. Journal of Economic Research (Tahghighat- E- Eghtesadi), 55(4), 963-990. doi: 10.22059/jte.2021.322088.1008455 (in persian)
Yunus, N. (2020). Time-varying linkages among gold, stocks, bonds and real estate. The Quarterly Review of Economics and Finance, 77, 165-185. | ||
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