نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده علوم ریاضی دانشگاه شهید بهشتی، تهران

2 کارشناس ارشد بیمه، دانشکده اکو دانشگاه علامه طباطبایی، تهران

3 دانشکده اکو دانشگاه علامه طباطبایی، تهران

چکیده

با استفاده از فاکتورهای اقتصادی، تحقیقات زیادی در مورد رفتار بازارهای بورس انجام گرفته است. این عوامل اقتصادی را می‌توان با استفاده از ابزار آماری با نام مفصل به طورهمزمان مورد بررسی قرار داد. مقاله حاضر تاثیر قیمت جهانی نفت و طلا را بر روی شاخص بورس تهران، با استفاده از یک مدل آریما-مفصل، مورد بررسی قرارمی دهد. این مقاله ابتدا با استفاده از اطلاعات دی 1376 الی دی 1389 یک مدل آریما- مفصل به داده‌ها برازش داده، سپس با استفاده از معیار اعتبار متقابل که به کمک داده‌ها دی 1389 الی تیر 1390 محاسبه می شود، مناسب بودن مدل از نظر قدرت پیش بینی مورد سنجش قرار می‌دهد. با استفاده از مدل برازش شده می‌توان نتیجه گرفت: (1) هیچ ارتباط مستقیم معنی داری بین قیمت جهانی طلا و شاخص بورس تهران وجود ندارد، (2) شاخص بورس تهران مستقل از قیمت جهانی نفت نبوده، بلکه با توجه به مفصل کلایتون می توان همبستگی کمی در دم‌ها را نتیجه گرفت. به عبارت دیگر کاهش قیمت نفت تاثیر معکوس بر روی شاخص بورس تهران و اقتصاد ایران می‌گذارد.

کلیدواژه‌ها

Abbasian, E. A., Moradpour Ouladi, M., and Abbasioun, V. (2008). The impact of macroeconomic variables on the SE: Evidence from Tehran Stock Exchange. Iranian Economic Research, 36, 135-152.
Arouri, M. (2011). Does crude oil move SE in Europe? A sector investigation. Economic Modeling,28, 1716-1725.
Arouri, M., Lahiani, A., and Ngugen, D. (2011). Return and volatility transmission between world oil prices and SEs of the GCC countries. Economic Modeling, 28, 1815-1825.
Arouri, M. and Rault, C. (2010). Oil prices and SEs: What drives what in the Gulf Corporation Council Countries? ESIFO working paper No. 2934. Category 10: Energy and Climate Economics.
Basher, S. A. and Sadorsky, P. (2006). Oil price risk and emerging SEs. Global Finance Journal, 17, 224–251.
Bashiri, N. (2011). The study of relationship between stock exchange index and gold price in Iran and Armenia. 5, http//:www.armef.com/pdfs/Neda_Bashiri.pdf/.
Brockwell, P. J. and Davis, R. A. (2009). Time Series: Theory and methods.  New York: Springer.
Cherubini U., Luciano E., and Vecchiato W. (2004). Copula methods in finance,  England: Wiley.
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, 141–151.
Constantinos, K., Ektor, L., and Dimitrios, M. (2010). Oil price and SE linkages in a small and oil dependent economy: the case of Greece. The Journal of Applied Business Research,26, 55–63.
Denuit, M., Dhaene, J., Goovaerts, M., and Kaas, R. (2005). Actuarial theory for dependent risks, measures, orders and models, Wiley, England.
El-Sharif, I., Brown, D., Burton, B., Nixon, B., and Russel, A. (2005). Evidence on the nature and extent of the relationship between oil prices and equity values in the UK. Energy Economics, 27, 819–830.
Eslamlouian, K. and Zare, H. (2007). The impact of macro variables and alternative assets on stock price movement in Iran: An ARDL model. Iranian Economic Research,29, 17-46.
Farzanegan, M. R. and Markwardt, G. (2009). The effect of oil price shocks on the Iranian economy. Energy Economics,31, 134–151.
Foster, K. and Kharazi, A. (2008). Contrarian and momentum returns on Iran’s Tehran Stock Exchange, The Journal of International Financial Markets. Institutions and Money,18, 16–30.
Frees, E. W. and Valdez, E. A. (1998). Understanding relationships using copulas. North American Actuarial Journal, 1, 1–25.
Frees, E. W. and Wang, P. (2005). Credibility using copula. North American Actuarial Journal, 2, 31–48.
Furstenberg, G. M. and Bang, N. J. (1989). International stock price movements: links and messages. Brookings Papers on Economic Activity, 1, 125–179.
Genest, C. and Favre, A. C. (2007). Everything you always wanted to know about copula but were afraid to ask. Journal of Hydrologic Engineering, 12, 347–368.
Genest, C. Rémillard, B. and Beaudoin, D. (2009). Goodness-of-fit tests for copulas: A review and a power study. Insurance: Mathematics and Economics,44, 199–213.
Grégoire, V., Genest, C., and Gendron, M. (2008). Using copulas to model price dependence in energy market. Energy Risk,5, 58–64.
Hammoudeh, S., and Aleisa, E. (2004). Dynamic relationships among GCC SEs and Nymex oil futures. Contemporary Economic Policy, 22, 250–269.
Hammoudeh, S., and Choi, K. (2006). Behaviour of GCC SEs and impacts of US oil and financial markets. Research in International Business and Finance, 20, 22–44.
Harvey, A. C. (1993). Time series models 2ed, The MIT Press, UK.
Hastie, T., Tibshirani R., and Friedman, J. (2009). The elements of statistical learning, data mining, inference, and prediction, 2nd ed., Springer, USA.
IMF (2004). Islamic Republic of Iran: Selected Issues, IMF Country Report No. 04/308.
IMF (2010). Islamic Republic of Iran: Selected Issues, IMF Country Report No. 10/76.
IMF (2011). Islamic Republic of Iran: Selected Issues, IMF Country Report No. 11/242
Jawadi F., Arouri M., and Bellalah M. (2010). Nonlinear linkages between oil and SEs
in developed and emerging countries. International Journal of Business, 15, 19–31.
Jondeau, E., and Rockinger, M. (2006). The Copula-GARCH model of conditional dependencies: An international SE application. Journal of International Money and Finance, 25, 827–853.
Keshavarz, H. G., and Manavi, S. H. (2009). SE and exchange rates interactions with respect to oil shocks. Iranian Economic Research, 37, 147-169.
Kilian, L., and Park, C. (2009). The impact of oil price shocks on the US SE. International Economic Review, 4, 1267–1287.
Kojadinovic, I., and Yan, J. (2010). Modeling multivariate distributions with continuous margins using the copula R package.  Journal of Statistical Software, 9, 1–20.
Lehmann, E. L., and Romano, J. P. (2005). Testing statistical hypothesis, 3rd ed. Springer, New York, USA.
Lin, C., Fang, C., and Cheng, H. (2009). Relationship between oil price shocks and SE: An empirical analysis from the Greater China.
Malik, F., and Hammoudeh, S. (2007). Shock and volatility transmission in the oil, US and Gulf equity markets. International Review of Economics and Finance, 16, 357–368.
Mashayekh, S., Haji Moradkhani, H., and Jafari, M. (2011). Impact of macroeconomic variables on SE: The case of Iran. 2nd International conference on business and economic research (2nd ICBER 2011).
Masron, T. A., and Fereidouni, H.G. (2010). Performance and diversification benefits of housing investment. International Journal of Economics and Finance, 4, 7–11.
Melo Mendes, B. V., and Aiube, C. (2011). Copula based models for serial dependence. International Journal of Managerial Finance, 7, 68–82.
Mohanty, S., Nandha, M., and Bota, G. (2010). Oil shocks and stock returns: The case of the Central and Eastern European (CEE) oil and gas sectors. Emerging Markets Review, 11, 358–372.
Mun, J. (2006). Real options analysis: Tools and techniques for valuing strategic investments and decisions. 2nd ed. Wiley Finance.
Nandha, M., and Hammoudeh, S. (2007). Systematic risk, and oil price and exchange rate sensitivities in Asia-Pacific SEs. Research in International Business and Finance,21, 326–341.
Nelsen, R. (2006). An introduction to copulas,2nd, Springer, New York.
Ning, C. (2010). Dependence structure between the equity market and the foreign exchange market- A copula approach. Journal of International Money and Finance, 29, 743–759.
Papachristou, D. (2004). Modelling dependencies. The Actuary, April, 24–27.
Park, J. W., and Ratti, R. A. (2007). Oil price shocks and SEs in the US and 13 European countries, Department of Economics, University of Missouri-Colombia, USA.
Pesaran, M. H., and Shin, Y. (1999). An autoregressive distributed lag modeling approach to cointegration analysis. (ed) S. Strom, Econometrics and Economic Theory in the 20th Century: The Ragner Frisch Centennial Symposium, chapter 11.Cambridge University Press, Cambridge.
Roch, O., and Alegra, A. (2006). Testing the bivariate distribution of daily equity returns using copulas. An application to the Spanish SE. Computational Statistics and Data Analysis, 51, 1312–1329.
Reboredo, J. (2011). How do crude oil prices co-move? A copula approach. Energy Economics, 33, 948-955.
Saeidi, P., and Amiri, A. (2009). Relation between macroeconomic variables and general index in Tehran Stock Exchange. Economic Modeling,(Iran), 2, 111-130.
Samadi, S., Shirani, Z., and Davarzadeh, M. (2007). Investigating the influence of world price of gold and oil on the Tehran Stock Exchange Index: modeling and forecasting. Quarterly Journal of Quantitative Economics,4, 25–51.
Wang, K., Chen, Y., and Huang, S. (2011). The dynamic dependence between the Chinese market and other international SEs: A time-varying copula approach. International Review of Economics and Finance,20, 654–664.
Zare, H., and Rezaei, Z. (2006). The effects of foreign exchange, gold coin and housing markets on Tehran SE: A vector error correction model. Research Bulletin of Isfahan University (Humanities),21, 99–112.
Zhang, Y., and Wei, Y. (2010). The crude oil market and the gold market: Evidence for cointegration, causality and price discovering. Resources Policy,35, 168–177.
http://www.cbi.ir/.
http://www.indexmundi.com/.
http://www.tse.ir/.
http://tsd.cbi.ir/.
R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.