Investigating return and volatility spillovers among selected industries of the Iranian stock market: TVP-VAR Extended Joint and DCC-GARCH approaches

Subject Areas : Financial Economics

Hadi Esmaeilpour Moghadam ¹ (Assistant Professor, Department of Economics, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran, (Crossponding Authur), [e-mail])
Emad Sharifbagheri ² (Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran,)

Received: 2024-07-24 Accepted : 2024-09-23 Published : 2024-10-05

Keywords: Spillover, Stock, TVP-VAR Extended Joint, Dynamic Conditional Correlation, Risk JEL Classification: C32, C58, G11,

Abstract :

Understanding the transmission of shocks and fluctuations among financial assets is crucial for effective risk management by investors. This study analyzes the dynamics of return spillovers and turbulence propagation across 15 industries in the Tehran Stock Exchange from October 12, 2009, to May 15, 2024. Employing the extended joint vector autoregression framework with time-varying parameters (TVP-VAR Extended Joint), we examine return spillovers, while turbulence spillovers are investigated using the dynamic conditional correlation (DCC-GARCH) approach. Our findings reveal several insights: Firstly, return shocks and turbulence in the network exhibit distinct dynamics. Secondly, the systemic risk resulting from turbulence transmission indicates tighter and more intricate interconnections among industries compared to return transmission. Thirdly, apart from the dominant investment industry, which serves as a major transmitter of shocks and fluctuations in both analyses, the pharmaceutical, construction, and food industries also emerge as significant net transmitters. Finally, the study confirms a strong dynamic synergistic relationship between basic metals and metal minerals throughout the study period.

References:

- ابونوری، اسمعیل و ضیاء¬الدین، حامد (1399). بازدهی و تلاطم بین قیمت جهانی نفت و شاخص بازار سهام در کشورهای عضو اوپک. مدلسازی اقتصادی، 14(49)، 24-1.
- علمی، زهرا، ابونوری، اسمعیل، راسخی؛ سعید و شهرازی، محمدمهدی (1393). اثر شکست‏های ساختاری در نوسانات بر انتقال تکانه و سرریز نوسان میان بازارهای طلا و سهام ایران. مدلسازی اقتصادی، 8(26)، 73-57.
- زراعتی، منصوره، صوفی مجیدپور، مسعود، محمودزاده، محمود و فتح¬آبادی، مهدی (1402). آثار نامتقارن تکانه‌های بازار سهام بر بازار ارز در ایران: کاربردی از مدل خودهم‌بستگی پویای شرطی و APARCH. مدلسازی اقتصادی، 17(62)، 58-37.
- دهباشی، وحید، محمدی، تیمور، شاکری، عباس و بهرامی، جاوید (1399). واکنش بازارهای ارز، سهام و طلا نسبت به تکانه های مالی در ایران: با تاکید بر اثرات سرریز تلاطم. پژوهشهای اقتصادی ایران، 25(83)، 27-1.
- محمدی¬نژاد پاشاکی، محمدباقر، صادقی شریف، سیدجلال و اقبال¬نیا، محمد (1402). بررسی و تحلیل اثرهای سرریز بین بازارهای سهام، ارز، طلا و کامودیتی: مدل VARMA-BEKK-AGARCH. تحقیقات مالی، 25(1)، 109-88.
- احمدی، حمیدرضا، هاشمی¬نژاد، سیدمحمد و محمودی، محمد (1404). بررسی سرریز ریسک بازار رمز ارزها با بازارهای مالی داخلی. دانش سرمایه¬گذاری، 14(53)، 574-551.
- محسنی، حسین و اعرابی، عارف (1402). اثر سرریز بازارهای گاز طبیعی، نفت خام و ارز بر شاخص سهام شرکت‌های تولید برق در ایران. فصلنامه پژوهش¬های سیاست¬گذاری وبرنامه¬ریزی انرژی، 9(4)، 33-1.
- Aboonouri, E. & Ziaoddin, H. (2020). Return and Volatility of International Oil Price and Stock Index in OPEC Member Countries. Economic Modeling, 14(49), 1-24. (in Persian)
- Adekoya, O. B. & Oliyide, J. A. (2021). How COVID-19 drives connectedness among commodity and financial markets: evidence from TVP-VAR and causality-inquantiles techniques. Resources Policy, 70, 101898.
- Ahmadi, H. R., Hasheminezhad, S. M. & Mahmoodi, M. (2025). Investigating the risk spillover of cryptocurrency market with domestic financial markets. Investment Knowledge, 14(53), 551-574. (in Persian)
- Andries, A. M. & Galasan, E. (2020). Measuring financial contagion and spillover effects with a state-dependent sensitivity value-at-risk model. Risks, 8(1), 5.
- Antonakakis, N., & Gabauer, D. (2017). Refined measures of dynamic connectedness based on TVP-VAR (MPRA Paper 78282). Munich, Germany: University Library of Munich.
- Antonakakis, N., Gabauer, D., Gupta, R., & Plakandaras, V. (2018). Dynamic connectedness of uncertainty across developed economies: A time-varying approach. Economics Letters, 166, 63–75.
- 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.
- Antonakakis, N., Chatziantoniou, I., Floros, C. & Gabauer, D. (2018). The dynamic connectedness of UK regional property returns. Urban Studies, 55(14), 3110–3134.
- Awartani, B. & Maghyereh, A. I. (2013). Dynamic spillovers between oil and stock markets in the gulf cooperation council countries. Energy Economics, 36, 28–42.
- Balcilar, M., Gabauer, D. & Umar, Z. (2021). Crude Oil futures contracts and commodity markets: new evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73, 102219.
- Basher, S. A., Haug, A. A. & Sadorsky, P. (2018). The impact of oil-market shocks on stock returns in major oil-exporting countries. Journal of International Money and Finance, 86, 264–280.
- Billio, M., Getmansky, M., Lo, A.W. & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of financial economics, 104(3), 535–559.
- Bisias, D., Flood, M., Lo, A. W. & Valavanis, S. (2012). A survey of systemic risk analytics. Annu. Rev. Financ. Econ., 4(1), 255–296.
- Bollerslev, T. (1986), Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
- Bouri, E., Cepni, O., Gabauer, D. & Gupta, R., (2021). Return connectedness across asset classes around the COVID-19 outbreak. International review of financial analysis, 73, 101646.
- Beirne, J., Caporale, G. M., Schulze-Ghattas, M., & Spagnolo, N. (2013). Volatility spillovers and contagion from mature to emerging stock markets. Review of International Economics, 21(5), 1060–1075.
- Briere, M., Chapelle, A. & Szafarz, A., (2012). No contagion, only globalization and flight to quality. Journal of international Money and Finance, 31(6), 1729–1744.
- Chatziantoniou, I., Gabauer, D., et al., (2021). EMU-Risk Synchronisation and Financial Fragility Through the Prism of Dynamic Connectedness. Technical Report, University of Portsmouth, Portsmouth Business School, Economics and Finance.
- Dehbashi, V., Mohammadi, T. , Shakeri, A. & Bahrami, J. (2020). The Responses of Stock, Gold and Foreign Exchange Markets to Financial Shocks: VAR-MGARCH Approach. Iranian Journal of Economic Research, 25(83), 1-27. (in Persian)
- Diebold, F. X. & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171.
- Diebold, F. X. & Yilmaz, K. (2012). Better to give than to receive: predictive directional measurement of volatility spillovers. International Journal of forecasting, 28(1), 57-66.
- Diebold, F. X. & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of econometrics, 182(1), 119-134.
- Dua, P. & Tuteja, D. (2016). Financial crises and dynamic linkages across international stock and currency markets. Economic Modelling, 59, 249–261.
- Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business & Economic Statistics, 20(3), 339–350.
- Elmi, Z., Aboonouri, E., Rasekhi, S. & Shahrazi, M. M. (2014). The Influence of Volatility Structural Changes on Shock Transmission and Volatility Spillover between Gold and Stock Markets in Iran. Economic Modeling, 8(26), 57-73. (in Persian)
- Fang, S., & Egan, P. (2018). Measuring contagion effects between crude oil and Chinese stock market sectors. The Quarterly Review of Economics and Finance, 68, 31–38.
- Gabauer, D. & Gupta, R. (2018). On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach. Economics Letters, 171, 63-71.
- Gabauer D. (2020). Volatility impulse response analysis for DCC-GARCH models: The role of volatility transmission mechanisms. Journal of Forecasting, 39(5), 788-796.
- Hoesli, M., & Reka, K. (2013). Volatility spillovers, comovements and contagion in securitized real estate markets. Journal of Real Estate Finance and Economics, 47(1), 1–35.
- 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.
- Lastrapes, W. D. & Wiesen, T. F., (2020). The joint spillover index. Economic Modelling, 94, 681-691.
- Le Pen, Y. & Sévi, B. (2010). Volatility transmission and volatility impulse response functions in European electricity forward markets. Energy Economics, 32(4), 758–770.
- Maggi, M., Torrente, M.-L. & Uberti, P. (2020). Proper measures of connectedness. Annals of Finance, 16(4), 547–571.
- Mishra, A. K., & Ghate, K. (2022). Dynamic connectedness in non-ferrous commodity markets: evidence from India using TVP-VAR and DCC-GARCH approaches. Resources Policy, 76, 102572.
- Mohammadinejad Pashaki, M. B., Sadeghi Sharif, S. & Eghbalnia, M. (2023). Investigating and Analyzing the Spillover Effects among Stock, Currency, Gold, and Commodity Markets: VARMA-BEKK-AGARCH Approach. Financial Research, 25(1), 88-109. (in Persian)
- Mohseni, H. & Aarabi A. (2024). The spillover effect of natural gas, crude oil, and currency markets on the electricity utilities sector index in Iran. Quarterly Journal of Energy Policy and Planning Research, 9(4), 1-33. (in Persian)
- Nazlioglu, S., Erdem, C., & Soytas, U. (2013). Volatility spillover between oil and agricultural commodity markets. Energy Economics, 36, 658–665.
- Oztek, M. F. & Ocal, N. (2017). Financial crises and the nature of correlation between commodity and stock markets. International Review of Economics & Finance, 48, 56–68.
- Pesaran, H. H. & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics letters, 58(1), 17–29.
- Philippas, D. & Siriopoulos, C. (2013). Putting the “C” into crisis: contagion, correlations and copulas on EMU bond markets. Journal of International Financial Markets, Institutions and Money, 27, 161–176.
- Torrente, M. L. & Uberti, P. (2021). Connectedness versus diversification: two sides of the same coin. Mathematics and Financial Economics, 15(3), 1–17.
- Wang, Y. S. & Chueh, Y. L. (2013). Dynamic transmission effects between the interest rate, the US dollar, and gold and crude oil prices. Economic Modelling, 30, 792–798.
- Xiao, B., Yu, H., Fang, L. & Ding, S. (2020). Estimating the connectedness of commodity futures using a network approach. Journal of Futures Markets, 40(4), 598–616.
- Zeraati, M., Soufi, M., Mahmoudzadeh, M. & Fathabadi, M. (2023). Asymmetric Effects of Stock Market shocks on Foreign Exchange Market in Iran: Application of DDC and APARCH Models. Economic Modeling, 17(62), 37-58. (in Persian)