تبیین و بررسی مدل‌ تلاطم و سرریز بازارهای جهانی محصولات پتروشیمی و فلزات اساسی (مبتنی بر مدل‌های خانواده کاپولا)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مالی، دانشکده مدیریت و اقتصاد، واحد علوم تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

2 گروه مالی، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

3 گروه مدیریت، دانشکده علوم اجتماعی و اقتصادی، دانشگاه الزهرا، تهران، ایران

4 گروه مدیریت صنعتی، دانشکده مدیریت، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران

چکیده

نوسانات قیمت‌ کالاها در بازارهای جهانی همواره بر رفتار و تصمیمات سرمایه‌گذاران در بازارهای مالی موثر بوده است. در این پژوهش با استفاده از مدل‌های خانواده کاپولا سرایت مالی یا سرریزی تلاطم بازارهای جهانی محصولات پتروشیمی و فلزات اساسی بر شاخص قیمت سهام شرکت‌های پذیرفته شده بر شاخص قیمت سهام هشت صنعت منتخب بورس اوراق بهادار تهران طی بازه زمانی 10 سال (96-1387) مورد بررسی قرار گرفته است. روش پژوهش از نظر ماهیت انجام تحلیلی- توصیفی و به لحاظ هدف کاربردی است. آزمون فرضیات پژوهش با استفاده از رهیافت اقتصادسنجی مبتنی بر مدل‌های کاپولا و برنامه‌نویسی در نرم‌افزار MATLAB انجام شد. نتایج نشان می‌دهد که اثرات سرریز این متغیرها بر شاخص صنایع منتخب معنی‌دار اما متفاوت می‌باشد.
بررسی مدل‌های مختلف روش کاپولا نشان داد که مدل‌ تی استیودنت بیشترین تناسب را در انتقال اثرات سرریز در دامنه‌های بالا و پایین دارند که این امر بیانگر وجود اثرات متقارن متغییرهای قیمت بازارهای جهانی محصولات پتروشیمی و فلزات اساسی دارای بر رفتار شاخص صنایع منتخب بورسی می‌باشد. و پس از آن مدل‌های کلایتون و گامبل در رتبه بعدی قرار دارد.

کلیدواژه‌ها

عنوان مقاله [English]

The Analysis and Test of Spillover and Volatility of Global Markets for Petrochemical Products and Base Metals (Based on Copula family models)

نویسندگان [English]

  • Mahsa Banakar 1
  • Hashem nikoomaram 2
  • Hasan Ghalibaf Asl 3
  • Mehrzad Minouie 4
1 Department of Finance, Science and Research Branch, Islamic Azad university, Tehran, Iran
2 Department of Finance, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 Department of Management, Alzahra University, Tehran, Iran
4 Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
چکیده [English]

Fluctuations in commodity prices in global markets have always influenced the behavior and decisions of investors in financial markets. In this research, using the Copula family models, financial contagion or volatility spillover on global price of petrochemical products and base metals on the on the stock price index of eight selected industries of Tehran Stock Exchange listed companies during a period of 10 years (2008-2018) has been reviewed. The research method is descriptive-analytical in nature and applied in terms of purpose. The research hypotheses were tested using an econometric approach based on Copula models and programming in MATLAB software. The results show that the effects of overflow of these variables on the index of selected industries are significant but different.
Examination of different models of Copula method showed that T-Student model is most suitable for transmitting spillover effects, which indicates the symmetrical effects of price variables in global markets of petrochemical products and base metals on the index performance of selected industries. And then Clayton and Gumble models are in the next rank.

کلیدواژه‌ها [English]

  • Financial Contagion
  • Volatility Spillover
  • Copula Functions
  • Global Markets

مراجع

  • Adrangi, B. Chatrah, A & Raffiee, K. (2014). Volatility spillovers across major equity markets of America. International journal of business, 19(3), pp. 255-273.
  • Antonakakis, N., & Kizys, R. (2015). Dynamic spillovers between commodity and currency markets. International Review of Financial Analysis, 41,pp 303-319.
  • Aragó-Manzana, V., & Fernández-Izquierdo, M. Á. (2007). Influence of structural changes in transmission of information between stock markets: A European empirical study, Journal of Multinational Financial Management, 17(2), pp. 112-124.
  • L., Laurent, S., and Rombouts, V. K. R. (2006). Multivariate Garch Models: A Survey, Journal of Applied Econometrics, vol. 21, pp. 79-109.
  • T., (1986). Generalized Autoregressive Conditional HeteroScedasticity, Journal of Econometrics, Vol. 31, No. 3, pp. 307- 327
  • Bonato, M., Caporin, M., & Ranaldo, A. (2013). Risk spillovers in international equity portfolios. Journal of Empirical Finance, 24, pp.121-137.
  • Calvo, S., and Reinhart, C. M. (1996). Capital flows to Latin America: Is there evidence of contagion effects, in Guillermo A. Calvo, Morris Goldstein, and Eduard Hochreiter, eds.: Private Capital Flows to Emerging Markets after the Mexican Crisis (Institute for International Economics, Washington, D.C.)
  • Chinzara, Z., (2011). Macroeconomic Uncertainty and Conditional Stock Market Volatility in South Africa, South African Journal of Economics, 79(1), pp. 27-49.
  • Clayton, D. G. (1978). A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika, 65(1), pp. 141-151.
  • Diebold, F. X., & Yilmaz, K (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1),pp 57-66.
  • Dornbusch, R., Park, Y., and Claessens, S. (2000). Contagion: understanding how it spreads. The World Bank Research Observer. 15, pp.177–197
  • Embrechts, P., McNeil, A., (2002), Correlation and dependence properties in risk management: properties and pitfalls, Dempster, M. (ed.) Risk Management: Value at Risk and Beyond, Cambridge University Press, pp.176-223.
  • Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK Inflation. Econometrica, 50, pp.987–1008
  • Ewing, B. T., & Malik, F. (2017). Modelling asymmetric volatility in oil prices under structural breaks. Energy Economics, 63, pp. 227-233.
  • Fattahi, Sh, Soheili, K and Dehghan Jabarabadi, Sh, (2017). Investigating the spread in Iran's financial markets using a combination of the Orenstein Olenbeck process and continuous wave conversion, Quarterly Journal of Econometric Modeling, Vol 4, pp. 53-33.
  • Frank, M. J., Nelsen, R. B., & Schweizer, B. (1987). Best-possible bounds for the distribution of a sum a problem of Kolmogorov. Probability theory and related fields, 74(2), pp.199-211
  • Gholami, N. Mohammadi, T. Ghasemi, A. (2020) Design a Model for Measuring the Dynamics Volatility Connectedness of Tehran Stock Exchange and Global Markets, Quarterly Journl of Economic Modelling, Vol 14, pp .49-71. (In Persian)
  • Ghorbanloo, Fatemeh, (2010) Modeling and Measuring Credit Portfolio Risk with Extreme Dependence, Master Thesis, Zanjan University (In Persian).
  • Gumbel, E. J. (1960). Bivariate exponential distributions. Journal of the American Statistical Association, 55(292), pp.698-707.
  • Heyrani, GH, M keshavarz Haddad. (2015). Estimation of Value at Risk in the Presence of Dependence Structure in Financial Returns: A Copula Based Approach, Journal of Economic Research (Tahghighat-E-Eghtesadi),49(4), pp. 869-902. (In Persian)
  • Hosseni Ebrahimabad, S. A, Jahangiri, Kh., Hasan 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 the Applied Economics Studies, Vol8, pp.123-155. (In Persian)
  • Kilian, L. (2009). Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. American Economic Review 99. pp. 1053-1069.
  • Kodres, L. E., and Pritsker, M. (2002). A rational expectations model of financial contagion, Journal of Finance 57, pp. 769-799.
  • Krugman, P. (1983). Oil and the dollar. NBER Working Paper No. 0554.
  • Mohseni, H. Sadeghi Shahdani, M.(2019). Exchange Rate Volatility Spillovers to Iran Capital Market, Journal of Applied Theories of Economics No. 20, pp.77-96. (In Persian)
  • Morales-Zumaquero, A., & Sosvilla-Rivero, S. (2016). Volatility Spillovers between Foreign-Exchange and Stock Markets. The Quarterly Review of Economics and Finance. Volume 70, pp. 121-136
  • Nikomaram, H. Pourzamani, Z. Dehghan, A. (2015). Spillover Effect the on Import & Export oriented industries. Financial Knowledge of Securities Analysis, 8(25), pp. 1-18 (In Persian).

*       Reboredo, J.C. Rivera-Castro, M.A., Ugolini, A, (2016). Downside and upside risk spillovers between exchange rates and stock prices, Journal of Banking & Finance, Vol 62, pp. 76-96

  • Seyed Hosseini, S M, Ebrahimi, S B, Babakhani, M (2013). Correlation Turbulence Model Fixed Condition with Long-Term Memory Evidence from Tehran and Dubai Stock Markets, Journal of Financial Engineering and Securities Management, 3 (11) pp.25 – 46
  • Sklar, A.(1959). Fonctions de répartition à n dimensions et leurs marges, Publ. Inst. Statist. Univ. Paris, 8, pp. 229–231
  • Song P.X.-K. (2000). Multivariate dispersion models generated from Gaussian copula, Scandinavian Journal of Statistics, 27(2): pp 305–320.
  • Turgut Tursoy, Faisal Faisal (2018). The impact of gold and crude oil prices on stock market in Turkey: Empirical evidences from ARDL bounds test and combined cointegration Resources Policy, Volume 55, pp. 49-54
  • Xiong, Z. & Han, L. (2015). Volatility spillover effect between financial markets: evidence since the reform of the RMB exchange rate mechanism. Financial Innovation, Volume 1(1).
  • Yu, L., Zha, R., Stafylas, D., He, K., & Liu, J. (2019). Dependences and volatility spillovers between the oil and stock markets: new evidence from the copula and VAR-BEKK-GARCH models. International Reviewof Financial Analysis Vol. 23, pp.117-129.
  • Zhang, Fan & Tsai, Wei (2008). Spillover Effect of US Dollar Exchange Rate on Oil Prices, Journal of Policy Modeling, Vol. 30, pp. 973-991.

 

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