Modeling the Dependency Structure between Stocks of Chemical Products Return, Oil Price and Exchange Rate Growth in Iran; an Application of Vine Copula

Sayadi, Mohammad; Karimi, Nasim

doi:10.29252/jemr.10.38.45

Volume 10, Issue 38 (12-2019) jemr 2019, 10(38): 45-94 | Back to browse issues page

‎ 10.29252/jemr.10.38.45

‎ 20.1001.1.22286454.1398.10.38.4.5

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Sayadi M, Karimi N. Modeling the Dependency Structure between Stocks of Chemical Products Return, Oil Price and Exchange Rate Growth in Iran; an Application of Vine Copula. jemr 2019; 10 (38) :45-94
URL: http://jemr.khu.ac.ir/article-1-1811-en.html

Modeling the Dependency Structure between Stocks of Chemical Products Return, Oil Price and Exchange Rate Growth in Iran; an Application of Vine Copula

Mohammad Sayadi ^*

¹, Nasim Karimi²

1- Kharazmi University , m.sayadi@khu.ac.ir
2- Islamic Azad University

Abstract: (4683 Views)

The main objective of this study is modeling the dependency structure between the returns of oil markets, exchange rate and stocks of chemical products in Iran. For this purpose, the theory of Vine Copula functions is used to investigate the dependency structure. In addition to consider a linear relationship between financial markets in Iran, the nonlinear dependency structure of these markets is also estimated, and their dependence on their upper or lower tails is determined. The study period includes daily data (5 working days) from December 2008 to July 2017. Modeling of marginal distributions of GJR-GARCH models has been used. Then, using the Copula-GARCH approach, the structure of dependency between returns and the calculating of the Value at Risk (VaR) of crude oil, exchange rate and stock of the chemical product group returns have been investigated. Finally, the required back-test is performed on the basis of the loss function. The study findings show that both pairs of modeling returns are related to the same upper and lower tails. In addition, there is a same structural dependency on the distribution of the vine copula between the indexes of chemical products and the nominal exchange rate on the condition of the price of crude oil, which indicates the spillover between markets. Due to that spillover effect is the main source of financial risk, the structural dependence on the basis of vine copula functions makes accurate and reliable calculation of portfolio risk based on the VaR criterion.

Keywords: Value At Risk, Vine Copula, Risk Spillover, Back-Testing, Dependence Structure

Full-Text [PDF 3514 kb] (1920 Downloads)

Type of Study: Applicable | Subject: انرژی، منابع و محیط زیست
Received: 2019/02/4 | Accepted: 2019/07/27 | Published: 2020/04/23

References

1. Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723. [DOI:10.1109/TAC.1974.1100705]

2. Ang, A. & J. Chen. (2002). "Asymmetric Correlations of Equity Portfolios", Journal of [DOI:10.1016/S0304-405X(02)00068-5]

3. Financial Economics, PP. 443-494.

4. Barghi Osguei, M.H., Motafaker Azad., M. A., Shahbazzadeh, A. (2014). Modeling nonlinear effects of the changes in real exchange rate and crude oil prices on Tehran stock exchange (The Markov Switching approach). Jemr, 4 (14), 85-109. {In Persian}

5. Basher, S. A., & Sadorsky, P. (2006). Oil price risk and emerging stock markets. Global finance journal, 17(2), 224-251. [DOI:10.1016/j.gfj.2006.04.001]

6. Beytari, J., panahian, H. (2019). Providing a model of trading volume relationships, transaction value with stock returns and price bubbles in different industries of Tehran Stock Exchange by using Copula functions and GARCH models, Financial Engineering and Portfolio Management,10(39), 26-53. {In Persian}

7. Bordbar N, Heidari E. (2017). The Effect of World Oil Price Fluctuations on the Return of the Energy Intensive Industries Stock in Iran. Jemr, 7 (27),177-205. {In Persian} [DOI:10.29252/jemr.7.27.177]

8. Cherubini, U., Luciano, E., & Vecchiato, W. (2004). Copula methods in finance John Wiley and Sons. New York. [DOI:10.1002/9781118673331]

9. 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), 141-151. [DOI:10.1093/biomet/65.1.141]

10. Durrleman, V., Nikeghbali, A., & Roncalli, T. (2000). A simple transformation of copulas. Available at SSRN 1032543. [DOI:10.2139/ssrn.1032543]

11. Emamverdi, GH. (2018). Studying the effects of using GARCH-EVTCOPULA method to estimate value at risk of portfolio, Iranian Journal of Finance. 2(1), 93-119. {In Persian}

12. Embrechts, P., Mcneil, A., Straumann, D. (1999), Correlation: Pitfalls and Alternatives, RISK Magazine, PP. 69-71.

13. Embrechts, P., Lindskog, F., & McNeil, A. J. (2001). Modelling Dependence with Copulas and Applications to Risk Management. Zürich.

14. Fakari Sardehae, B., Sabuhi, M., Shahpuri, A. (2018). The effects of changes in the price of crude oil on the Tehran Stock Exchange index: The use of M-GARCH approach BEKK, Journal of Economic Research (Tahghighat-E-Eghtesadi), 53(2), 387-407. {In Persian}

15. 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), 199-211. [DOI:10.1007/BF00569989]

16. Filis, G., Degiannakis, S. and CH. Floros (2011), "Dynamic Correlation between Stock Market and Oil Prices: The Case of OilImporting and Oil-Exporting Countries", International Review of Financial Analysis, Vol. 20, Issue.3, pp.152-164. [DOI:10.1016/j.irfa.2011.02.014]

17. Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801. [DOI:10.1111/j.1540-6261.1993.tb05128.x]

18. Gong, X. L., Liu, X. H., & Xiong, X. (2019). Measuring tail risk with GAS time varying copula, fat tailed GARCH model and hedging for crude oil futures. Pacific-Basin Finance Journal, 55, 95-109. [DOI:10.1016/j.pacfin.2019.03.010]

19. Gumbel, E. J. (1960). Bivariate exponential distributions. Journal of the American Statistical Association, 55(292), 698-707. [DOI:10.1080/01621459.1960.10483368]

20. Huang, J. Lee, K., Liang, H. and Lin, W. (2003). Estimating value at risk of portfolio by conditional copula-GARCH method. Insurance: Mathematics and Economics, 45, 315-324. [DOI:10.1016/j.insmatheco.2009.09.009]

21. Jalaei Esfanabadi, S.A., Salehi, N., Shivaei, E. (2018). Modeling the relationship between the price index in financial markets terms of trade in Iran (marton and conditional Copula functions approches), Journal of Financial Economics, 42(12), 1-24. {In Persian}

22. Jondeau, E. Rockinger, M. (2006). The Copula_GARCH model of conditional dependency: an international stock market application, Journal of International Moneyand Finance, 25, 827-853. [DOI:10.1016/j.jimonfin.2006.04.007]

23. Jorion, P. (2007). Financial risk manager handbook, (Vol. 406). John Wiley & Sons.

24. keshavarz Haddad, GH., Heyrani, M. (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), 869-902. {In Persian}

25. Le, T. H., & Chang, Y. (2015). Effects of oil price shocks on the stock market performance: Do nature of shocks and economies matter? Energy Economics, 51, 261-274. [DOI:10.1016/j.eneco.2015.06.019]

26. Ma, Ch.K., & Kao, G.W. (1990). On exchange rate change and stock price reactions. Journal of Business Finance and Accounting, 17(3): 441-449. [DOI:10.1111/j.1468-5957.1990.tb01196.x]

27. Mamipour, S., Feli, A. (2017). The Impact of Oil Price Volatility on Tehran Stock Market at Sector-Level: A Variance Decomposition Approach, Financial, Monetary Economics, 24(14), 205-236. {In Persian}

28. McNeil, A. J., Frey, R., & Embrechts, P. (2005). Quantitative risk management: Concepts, techniques and tools (Vol. 3). Princeton: Princeton university press.

29. Mousavi, M., Raghfar, H., Mohseni, Mansureh. (2013). Estimation of the Value of Risky Stocks (Using Conditional Copula-Garch Method), Iranian Journal of Economic Research, 18(54), 119-152. {In Persian}

30. Nandha, M., Faff, R. (2008), Does oil move equity prices? A global view, Energy Economics, 30, 986-997. [DOI:10.1016/j.eneco.2007.09.003]

31. Nelsen, R. B. (2006). An introduction to copulas, 2nd. New York: Springer Science Business Media.

32. Nikoo Eghbal, A., Alikhani, N., Naderi. E. (2013). The Analysis of Crude Oil Prices Dynamic Effects on Irans Methanol, Iranina journal of Energy ,16 (3), 91-106. {In Persian}

33. Palaro, H., Hotta, L. (2006). Using conditional copula to estimate Value at Risk. Journal of Data science,4.33-115.

34. Park, J. W. (2007). Oil price shocks and stock market behavior: empirical evidence for the US and European countries (Doctoral dissertation, University of Missouri-Columbia.

35. Pishbahar, E., Abedi, S. (2017) Measuring portfolio Value at Risk: The application of copula approach, Financial Engineering and Portfolio Management, 8(30), 55-73. {In Persian}

36. Rockinger, M., & Jondeau, E. (2001). Conditional dependency of financial series: an application of copulas. [DOI:10.2139/ssrn.1730198]

37. Sadeghi Shahdani, M., Mohseni, H. (2013). The effect of oil price on stock market returns: Evidence from oil exporting Middle East countries, Quarterly Journal of Energy Policy and Planning Research, 1 (3), 1-16. {In Persian}

38. Sadorsky. Perry and Haug. Alfred. A and Basher. Syed Abul, (2011), " Oil prices, exchange rates and emerging stock markets", MPRA Paper No. 30140, posted 07.

39. Sheng, Y., & Chyidoong, S.H. (2004). Price and volatility spillovers between stock prices and exchange rates: empirical evidence from the G-7 Countries. International Journal of Business and Economics, 3(2):139-153.

40. Sklar, M. (1959). Fonctions de repartition a dimensions et leurs marges. Publ. inst. statist. univ. Paris, 8, 229-231.

41. Wang, Z., Chen, X., Jin, Y. and Zhou, Y. (2010). Estimating risk of foreign exchange portfolio: Using VaR and CVaR based on GARCH-EVT-copula model. Physica A, 383. 4318-4328. [DOI:10.1016/j.physa.2010.07.012]

42. Wang, K., Chen, Y. -H., & Huang, S. -W. (2011). The dynamic dependence between the Chinese market and other international stock returns: A time-varying copula approach. International Review of Economics and Finance, 21, 654-664. [DOI:10.1016/j.iref.2010.12.003]

43. 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 Review of Financial Analysis. 23, 117-129. [DOI:10.1016/j.irfa.2018.11.007]

44. Zaroki, Sh., Motameniorcid, M., Fathollahzadeh, A. (2018). The Effect of the Global Oil Price on Value of the Petrochemical Industry in Iran with NARDL Approach, Journal of Iranian Energy Economics, 7(27), 101-132. {In Persian}