پژوهش ها و چشم اندازهای اقتصادی

پژوهش ها و چشم اندازهای اقتصادی

اولویت بندی واگذاری بنگاه های اقتصادی زیر مجموعه صندوق های بازنشستگی با تاکید بر مدیریت سرمایه گذاری: شواهدی جدید از رویکرد DCC-GARCH R2 decomposed connectedness

نوع مقاله : مقالات علمی پژوهشی

نویسندگان
حسن چنارانی 1
سهیل رودری 2
1 استادیار گروه اقتصاد و بانکداری اسلامی، دانشکده اقتصاد، دانشگاه خوارزمی، تهران، ایران
2 دکتری اقتصاد، گروه اقتصاد، دانشکده علوم اقتصادی و اداری، دانشگاه فردوسی، مشهد، ایران
10.48311/ecor.2025.115460.0
چکیده
در سال‌های اخیر، با توجه به کسری منابع صندوق‌های بازنشستگی و ناترازی مالی آن‌ها، موضوع اصلاح ساختار و بهینه‌سازی پرتفوی سرمایه‌گذاری این صندوق‌ها، اهمیت بیشتری یافته است. بر این اساس، در پژوهش حاضر، با در نظر گرفتن یک پورتفوی نوعی متشکل از نمادهای فباهنر، فخاس، جم و درازک در دوره زمانی 12/08/2025-01/01/2015 با استفاده از الگوی DCC-GARCH R2 decomposed connectednesss و از منظر مدیریت سرمایه­ گذاری، اولویت­ بندی واگذاری بنگاه­ های اقتصادی تحت مدیریت صندوق­ های بازنشستگی بررسی شده است. نتایج نشان داد که از ابتدای سال 1404 با توجه به تشدید تنش ها و ریسک های ژئوپلتیک، میزان ارتباط و اتصال میان دارایی­ ها افزایش یافته و امکان تنوع بخشی به پورتفو وجود ندارد و فرصت مناسبی جهت واگذاری بنگاه­ ها می­باشد. همچنین بر اساس نتایج، رویکرد حداقل ضریب تعیین (MRP)، دارای بالاترین نسبت شارپ می‌باشد و کمترین وزن بهینه را به‌ترتیب، برای فخاس و فباهنر در نظر گرفته است. بر این اساس و در راستای انجام تکالیف برنامه هفتم توسعه کشور، بهتر است فخاس و فباهنر سریع­تر ارزش­ گذاری شده و با طی فرایند قانونی واگذار شوند. نتایج پژوهش حاضر، می­تواند به­ عنوان یک معیار در ارزیابی و اولویت ­بندی واگذاری بنگاه­ های اقتصادی مدنظر سیاست‌گذاران باشد.
کلیدواژه‌ها
صندوق‌های بازنشستگی
مدیریت سرمایه‌گذاری
مدل DCC-GARCH R2 Decomposed Connectedness
موضوعات

عنوان مقاله English

Prioritizing the Divestment of Pension Fund–Owned Enterprises Through Portfolio Optimization: New Evidence from the DCC–GARCH R² Decomposed Connectedness Approach

نویسندگان English

Hasan Chenarani 1
Soheil Roudari 2
1 Assistant Professor, Department of Economics and Islamic Banking, Faculty of Economics, Kharazmi University, Tehran, Iran
2 PhD in Economics, Department of economics, Faculty of economics and administrative sciences, Ferdowsi University, Mashhad, Iran
چکیده English

Abstract
Pension funds in Iran, as intergenerational financial institutions, play a pivotal role in safeguarding retirees’ financial security. Over recent decades, these funds have encountered significant challenges stemming from government policies, macroeconomic crises, and managerial inefficiencies. A central concern has been the suboptimal performance of affiliated enterprises in generating value added and sustainably financing pension obligations. In light of persistent resource deficits and structural financial imbalances, reforming governance structures and optimizing investment portfolios have become increasingly urgent policy priorities. One criterion for valuing enterprises subject to privatization is market capitalization, which depends fundamentally on stock market performance. Accordingly, the optimal portfolio weight of each enterprise, hedging effectiveness, beta coefficient, and Sharpe ratio should be evaluated across alternative portfolio management strategies—including the Minimum Variance Portfolio (MVP), Minimum Correlation Portfolio (MCP), Minimum Connectedness Portfolio (MCOP), Minimum Bivariate R2 Portfolio (MRP), and Minimum R2 Decomposed Connectedness Portfolio (MPG). The model yielding the highest Sharpe ratio can then be identified as the optimal approach. Under such a framework, enterprises may be prioritized for accelerated or delayed divestiture based on their return–risk profiles, consistent with the mandates of the country’s Seventh Economic Development Plan. To date, this issue has not been examined using the aforementioned methodological approaches, despite its substantial policy relevance
Purpose/Aims:
The primary objective of this study is to determine the prioritization of divesting pension fund–owned enterprises using novel portfolio optimization techniques. The analysis draws on daily return data for the selected firms over a 10-year period and applies advanced econometric models to estimate their optimal portfolio weights. Based on performance indicators—most notably the Sharpe ratio—the study identifies divestiture priorities. Policymakers may thus target enterprises with lower optimal weights and inferior Sharpe ratios for earlier divestiture, an analytical dimension that has not previously been addressed in the literature.
Methodology & Framework:
This study employs the Time-Varying Parameter Vector Autoregression (TVP–VAR) model developed by Antonakakis, estimated via the Kalman filter, to analyze dynamic correlations among assets. Portfolio optimization is conducted using the Broadstock approach. The TVP–VAR framework, with time-varying coefficients, enables the identification of structural shocks and volatility spillovers. Derived measures—including the Generalized Impulse Response Function (GIRF), Generalized Forecast Error Variance Decomposition (GFEVD), Total Connectedness Index (TCI), and Pairwise Connectedness Index (PCI)—are utilized to assess portfolio sensitivity to shocks, the role of individual assets in risk transmission, and overall market connectedness.
In addition, the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity R-squared (DCC–GARCH R²) decomposed connectedness approach introduced by Cocca et al. (2024) is applied to estimate the MRP and MPG strategies. This integrated analytical framework provides detailed insights for dynamic and informed portfolio management, particularly in the context of state-owned enterprises.
Findings:
Conditional correlations among the examined symbols exhibit substantial time variation. Over the past year, FAKHAS displayed the lowest correlation with DERAZAK and the highest correlation with JAM. Conversely, JAM exhibited the lowest correlation with DERAZAK and the highest with FAKHAS. Over the past two years, FBAHONAR showed the highest conditional correlation with FAKHAS and the lowest with JAM. For DERAZAK, the minimum correlation occurred with JAM, while the maximum was observed primarily with FBAHONAR.
Overall, dynamic conditional correlations between FAKHAS and FBAHONAR and the other symbols were relatively high. This finding indicates that, in portfolios already containing JAM and DERAZAK, the inclusion of FAKHAS and FBAHONAR increases overall portfolio risk.
Regarding return transmission, FAKHAS and FBAHONAR emerged as the largest net transmitters of returns, whereas JAM and DERAZAK were identified as the largest net receivers.
In terms of hedging effectiveness, JAM exhibited negative hedging effectiveness, suggesting that the examined assets did not provide effective risk reduction for this asset. Under bullish market conditions, the DERAZAK/JAM combination demonstrated the highest hedging effectiveness, whereas FAKHAS/JAM exhibited the lowest. Under bearish conditions, the FAKHAS/FBAHONAR combination provided the highest hedging effectiveness, while DERAZAK/JAM showed the weakest performance.
Concerning the identification of the optimal portfolio strategy, the highest Sharpe ratio was achieved under the MRP approach. Accordingly, this method is identified as the optimal strategy for portfolio management and the prioritization of enterprise divestiture. Across all return scenarios, FAKHAS and FBAHONAR consistently received the lowest portfolio weights, indicating comparatively weak performance from a capital market perspective.
Discussion:
This study evaluates portfolio management and divestiture prioritization for four pension fund–owned enterprises—FBAHONAR, FAKHAS, JAM, and DERAZAK—over the period from January 1, 2015, to August 12, 2025, using the DCC–GARCH R2 Decomposed Connectedness framework proposed by Cocca et al. (2024).
The results demonstrate that FAKHAS and FBAHONAR exhibit relatively high dynamic conditional correlations with other assets, implying that their inclusion in portfolios containing JAM and DERAZAK increases aggregate risk exposure. Moreover, both FAKHAS and FBAHONAR function predominantly as net transmitters of returns within the connectedness network.
Conclusion & Implications:
From a comprehensive portfolio management perspective, the MRP approach yields the highest Sharpe ratio and is therefore identified as the optimal strategy for managing pension fund assets and prioritizing enterprise divestiture. Under this framework, FAKHAS and FBAHONAR consistently receive the lowest optimal weights across return scenarios, reflecting suboptimal performance in terms of risk-adjusted returns.
Given that return and risk constitute the fundamental pillars of investment decision-making, determining optimal asset weights under alternative portfolio strategies is essential. The findings of this study provide a rigorous and objective basis for prioritizing divestiture. Should sufficient institutional commitment to enterprise restructuring exist, the results offer a scientifically grounded criterion to guide policymakers in implementing divestment decisions consistent with national development objectives.

کلیدواژه‌ها English

DCC&ndash
GARCH.R²
Decomposed, Connectedness Approach, Investment Management, Pension Funds
References
Abounoori, E., Tehrani, R., & Sabouri, H. (2021). Risk spillover from financial sector to real sector using the conditional coincidence index (CCX): Case study of Iranian capital market. https://doi.org/10.30495/fed.2021.687868
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, 1-15. https://doi.org/10.1016/j.resourpol.2022.102877.
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.
     https://doi.org/10.1016/j.iref.2024.103492.
Ahmadian-Yazdi, F., Sokhanvar, A., Roudari, S., & Tiwari, A. K. (2025). Dynamics of the relationship between stock markets and exchange rates during quantitative easing and tightening. Financial Innovation, 11(1), 51.
     https://doi.org/10.1186/s40854-024-00694-4.
Ahmadian-Yazdi, F., Mesgarani, M., & Roudari, S. (2022). Natural resource rents and social capital interaction: New evidence on the role of financial development. Journal of Environmental Assessment Policy and Management, 24(01). https://doi.org/10.1142/S1464333222500211.
Ahmadian Yazdi, F., Ebrahimi Salari, T., Jandaghi, F., & Rajab Zadeh Moghani, N. (2015). Investigating the effective factors on human capital accumulation in Iran in the period 1971-2012. Journal of Applied Economics Studies in Iran, 4(15),201228.https://dor.isc.ac/dor/20.1001.1.23222530.1394.4.15.101. [In Persian]
Ahmadi, H. R., Hasheminezhad, S. M. and mahmoodi, M. (2024). Investigating the risk spillover of cryptocurrency market with domestic financial markets. Journal of Investment Knowledge, 14(53), 551-574. doi: 10.30495/jik.0621.23477 [In Persian]
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.
     https://doi.org/10.1016/j.eneco.2020.104741.
Alshater, M. M., Alqaralleh, H., & El Khoury, R. (2023). Dynamic asymmetric connectedness in technological sectors. The Journal of Economic Asymmetries, 27, 1-15. https://doi.org/10.1016/j.jeca.2022.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), 1-15.
     https://doi.org/10.3390/jrfm13040084.
Ashena, M. and La’l khezri, H. (2020). The dynamic correlation of global economic policy uncertainty index with stock, exchange rate and gold markets in Iran: Application of M-GARRCH and DCC approach. Journal of Econometric Modelling, 5(2), 147-172. doi: 10.22075/jem.2020.20667.1480 [In Persian]
Argha, L. , Mowlaei, M. and 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.
     https://doi.org/10.1016/j.econmod.2014.10.030.
Billah, M., Balli, F., & Hoxha, I. (2023). Extreme connectedness of agri-commodities with stock markets and its determinants. Global Finance Journal, 56, 100824. https://doi.org/10.1016/j.gfj.2023.100824.
Broadstock, D. C., Chatziantoniou, I., & Gabauer, D. (2022). Minimum Connectedness Portfolios and the Market for Green Bonds: Advocating Socially Responsible Investment (SRI) Activity. In Applications in Energy Finance: The Energy Sector, Economic Activity, Financial Markets and the Environment (pp. 217-253). Cham: Springer International Publishing.
     https://doi.org/10.1007/978-3-030-92957-2_9.
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.
     https://doi.org/10.1016/j.frl.2022.103070.
Chatziantoniou, I., Gabauer, D., & Stenfors, A. (2021). Interest rate swaps and the transmission mechanism of monetary policy: A quantile connectedness approach. Economics Letters, 204, 109891.
     https://doi.org/10.1016/j.econlet.2021.109891.
Dadmehr, M., Rahnama Roodposhti, F., Nikoumaram, H. and 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]  
Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119-134.
     https://doi.org/10.1016/j.jeconom.2014.04.012.
Ederington, L. H. (1979). The hedging performance of the new futures markets. Journal of Finance, 34(1), 157-170. https://doi.org/10.1111/j.1540-6261. 1979.tb02057.x.
Elliott, G., Rothenberg, T. J., & Stock, J. H. (1992). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813-836.
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. https://doi.org/10.1016/j.mulfin.2018.04.004.
Hamao, Y., Masulis, R. W., & Ng, V. (1990). Correlations in price changes and volatility across international stock markets. The review of financial studies, 3(2), 281-307.
Hoseini, A., Jahangiri, K., Heydari, H. and 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]
Hosseini, S. M. , Dadras, A. , Khzaei, S. and Razzaghi, M. (2020). Investigating the effect of financial ratios and optimal macroeconomic variables on stock returns of Shasta subsidiaries. Social Security Journal, 15(4), 81-100. [In Persian]
Jalayi Esfandabadi, S. A., Salehi Asfiji, N., & Shiavayi, E. (2018). Modeling the relationship between price indices in financial markets and terms of trade in the Iranian economy: A Merton jump model and conditional copula functions approach. Journal of Financial Economics, 42(12), 1-24. [In Persian]
Jabeur, S. B., Gozgor, G., Rezgui, H., & Mohammed, K. S. (2024). Dynamic dependence between quantum computing stocks and Bitcoin: Portfolio strategies for a new era of asset classes. International Review of Financial Analysis, 95, 103478. https://doi.org/10.1016/j.irfa.2024.103478.
Jiang,Y., Fu,Y., & Ruan, W. (2019).  Risk spillovers and portfolio management between precious metal and BRICS stock markets. Physica A, 534,120993. https://doi.org/10.1016/j.physa.2019.04.229.
Lang, C., Hu, Y., Corbet, S., & Goodell, J. W. (2023). Dynamic return connectedness between commodities and travel & leisure ETFs: Investment strategies and portfolio implications. Finance Research Letters, 58, 104371. https://doi.org/10.1016/j.frl.2023.104371.
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. https://doi.org/10.1016/j.resourpol.2021.102166.
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.
     https://doi.org/10.1016/j.iref.2021.10.003.
Megginson, W. L., & Netter, J. M. (2001). From state to market: A survey of empirical studies on privatization. Journal of Economic Literature, 39(2), 321-389. https://doi.org/10.1257/jel.39.2.321.
Markowitz, H. (1959). Portfolio selection. Journal of Finance, 7, 77-91.
Mensi, W., Al Rababa'a, A. R., Alomari, M., Vo, X. V., & Kang, S. H. (2022). Dynamic frequency volatility spillovers and connectedness between strategic commodity and stock markets: US-based sectoral analysis. Resources Policy, 79, 102976. https://doi.org/10.1016/j.resourpol.2022.102976.
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. https://doi.org/10.1016/j.ribaf.2024.102299.
Mohajeri, P. and 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]
Mohammadishad, H., Madanchi Zaj, M., Keyghobadi, A.R. (2021).Risk spillover and dynamics between financial markets, commodity markets and digital currencies with the MGARCH method. Financial Engineering and Portfolio Management, 47, 470-490. [In Persian]
Mohammadinejad Pashaki, M.B., Sadeghi Sharid, S.J., Eqbalnia, M. (2023).Investigating and analyzing the spillover effects of stock market in interaction with currency, gold-coin, crude oil and housing markets: VARMA-BEKK-AGARCH approach. Financial Engineering and Portfolio Management, 57,174-199. https://doi.org/10.22059/frj.2022.332526.1007248 [In Persian]
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]
Nguyen, N. H., Nguyen, H. D., VO, L. T. K., & Tran, C. Q. K. (2021). The impact of exchange rate on exports and imports: Empirical evidence from Vietnam. The Journal of Asian Finance, Economics and Business, 8(5), 61-68. https://doi.org/10.13106/jafeb.2021.vol8.no5.0061.
Omidi, V., Gudarzi farahani, Y. and Roudari, S. (2023). Investigating the quantile time-varying relationship between exchange rate, current account deficit, government budget deficit, and inflation in the Iranian economy. Journal of Econometric Modelling, 8(1), 129-157. doi: 10.22075/jem.2023.29051.1783 [In Persian]
Omidi, V. Roudari, S. & Jamshidi, A. (2024). 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, Journal of Financial Management Strategy, 12(1), 69-86.
      https://doi.org/10.22051/jfm.2024.43995.2830. [In Persian]
Petmezas, D., & Santamaria, D. (2014). Investor induced contagion during the banking and European sovereign debt crisis of 2007-2012: Wealth effect or portfolio rebalancing?. Journal of International Money and Finance, 49, 401-424. https://doi.org/10.1016/j.jimonfin.2014.06.005.
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. https://doi.org/10.1016/j.ribaf.2022.101810.
Reboredo, J. C., Ugolini, A., & Hernandez, J. A. (2021). Dynamic spillovers and network structure among commodity, currency, and stock markets. Resources Policy, 74, 102266. https://doi.org/10.1016/j.resourpol.2021.102266.
Roudari, S., Maghsoudi, H., & Ahmadian-Yazdi, F. (2025). Time-varying connectedness across exchange rate, inflation, budget deficit, and liquidity in Iran a TVP-TVAR approach. The Economic Research, 25(2), 141-170. [In Persian]
Roudari, S., Ahmadian-Yazdi, F., Arabi, S. H., & Hammoudeh, S. (2023a). Sanctions and Iranian stock market: Does the institutional quality matter?. Borsa Istanbul Review, 23(4), 919-935.
     https://doi.org/10.1016/j.bir.2023.03.006.
Roudari, S., Mensi, W., Al Kharusi, S., & Ahmadian-Yazdi, F. (2023b). Impacts of oil shocks on stock markets in Norway and Japan: Does monetary policy's effectiveness matter?. International Economics, 173, 343-358.
     https://doi.org/10.1016/j.inteco.2023.01.006.
Roudari, S., Jalili, E. & Omidi, V. (2023). Portfolio management in the refining industry: Investigating conditions with positive and negative returns: An asymmetric TVP-VAR approach. Financial Management Perspective, 13(43), 133-154. https://doi.org/10.48308/jfmp.2024.104291 .[In Persian]
Roudari, S., Farahanifard, S., Shahabadi, A. & Adeli, O. (2022). Investigating the time-frequency volatility spillover among exchange rate, inflation, stocks and housing prices in Iran. Journal of Economics and Modelling, 13(2), 65-93. doi: 10.29252/jem.2022.228781.1783. [In Persian]
Roudari, S., Farahanifard, S., Shahabadi, A. and Adeli, O. (2022). Investigating the time-frequency volatility spillover among exchange rate, inflation, stocks and housing prices in Iran. Journal of Economics and Modelling, 13(2), 65-93. https://doi.org/10.48308/jem.2022.228781.1783. [In Persian]
Safari Sarchah, F., Yazdifar, H., & Pifeh, A. (2020). Privatization, changes in management accounting practices and their impacts on financial performance-evidence from Iran. Iranian Journal of Finance, 4(3), 18-48.
     https://doi.org/10.22034/ijf.2020.231937.1129.
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.
Sezavar, M. R., Khazaei, A. and Eslamian, M. (2019). Conditional correlation between foreign exchange markets, gold, housing, stock and oil in the Iranian economy. Economic Strategy, 8(29), 37-60. [In Persian]
Shahbaz, M., Aboutorabi, M. A., & Yazdi, F. A. (2024). Foreign capital, natural resource rents and financial development: A new approach. Global Business Review, 25(2), 401-420. https://doi.org/10.1177/0972150920961364.
Sharif Karimi, M., Heidarian, M., Dehghan Jabar Abadi, S. (2018). Analysis of spillover effects between the oil market and Tehran stock exchange across multiple time scales: A Wavelet-Based VAR-GARCH-BEKK approach, Journal of Financial Economics, 12(42), 25-46. [In Persian]
Taleblou, R. and 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]
Tayebi, S. K. and Sadeghi, A. (2017). The impacts of international sanctions and other factors affecting exchange rate in Iran. Journal of Economic Research (Tahghighat- E- Eghtesadi), 52(3), 641-661. doi: 10.22059/jte.2017.63308 [In Persian]
Wang, Y., Zhao, X., & Shang, J. (2025). Dynamic risk spillover in green financial markets: A wavelet frequency analysis from China. Energy Economics, 143, 108301. https://doi.org/10.1016/j.eneco.2025.108301.
Yunus, N. (2020). Time-varying linkages among gold, stocks, bonds and real estate. The Quarterly Review of Economics and Finance, 77, 165-185.
     https://doi.org/10.1016/j.qref.2020.01.015.
Yousfi, M., & Bouzgarrou, H. (2025). Exploring interconnections and risk evaluation of green equities and bonds: Fresh perspectives from TVP-VAR model and wavelet-based VAR analysis. China Finance Review International, 15(1), 117-139. https://doi.org/10.1108/CFRI-05-2024-0237