عنوان مقاله [English]
In this paper, we apply R-Vine copula -ARMA-APGARCH approach to investigate the dynamic relationship between banking, insurance and pension, investment and other financials sub-indexes in Tehran stock exchange. Using a sample of more than 8 years of daily return observations of the financial sub-indexes, we find evidence of significant and symmetric relationship between these variables. Finally, there is evidence to suggest that the application of the vine copula model improves the accuracy of VaR estimates, compared to traditional approaches. This paper results show that vine copula VaR is accurate at 1% and 5% significance levels. This paper’s findings suggest the flexibility and capacity of vine copula structures in financial dependency modeling and risk management
* Aloui, R., & Ben Aissa, M. (2016). Relationship between oil, stock prices and exchange rates: A vine copula based GARCH method. The North American Journal of Economics and Finance, 37, 458-471.
* Applications, P. A. (2014). Forecasting VaR and ES of stock index portfolio: A Vine copula method. Physica A: Statistical Mechanics and its Applications, 416, 112-124.
* Armin, P., Kim, J., & Tafakori, L. (2016). Measuring systemic risk using vine-copula. Economic Modelling, 53, 63–74.
* BenSaida, A. (2017). The contagion effect in European sovereign debt markets: A regime-switching vine copula approach. International Review of Financial Analysis, in press.
* Brechmann, E., Czado, C., & Paterlini, S. (2014). Flexible dependence modeling of operational risk losses and its impact on total capital requirements. Journal of Banking & Finance, 40, 271-285.
* Choudhry, M. (2013). An introduction to Value at Risk. john Wiley& sons.
* Ding, Z., Granger, C., & Engle, R. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 98-106.
* Ding, Z., Granjer, C., & Engle, R. (1993). A long memory property of stock market returns and a new model. Journal of Emprical Finance, 83-106.
* Fischer, M., Schluter, C., & Weigert, F. (2009). An empirical analysis of multivariate copula models. Quantitative Finance, 9(7), 839-854.
* Flores, M. Ú., & et al. (2017). Copulas and Dependence Models with Applications. Switzerland: Springer.
* Hansen, B. (1994). Autoregressive conditional density estimation. International Economic Review, 35, 705-730.
* Kurowicka, D., & Joe, H. (2011). Vine Copula Handbook. Hong Kong: World Scientific Publishing.
* Mahfoud, M. (2012). Bivariate Archimedean copulas: an application to two stock market indices. Vrije Universiteit Amsterdam. Amsterdam.
* Nelsen, R. (2006). An Introduction to Copulas. Springer.
* Reboredo, J., & Ugolini, A. (2015). Downside/upside price spillovers between precious metals: A vine copula approach. North American Journal of Economics and Finance, 34, 84-102.
* Ruschendorf, L. (2013). Mathematical Risk Analysis. Springer.
* Sukcharoen, K., & Leatham, D. (2017). Hedging downside risk of oil refineries: A vine copula approach. Energy Economics, 66, 493-507.
* Yu, W., yung, K., & wei, Y. (2017). Measuring Value-at-Risk and Expected Shortfall of crude oil portfolio using extreme value theory and vine copula. Physica A: Statistical Mechanics and its Applications, 490, 1423-1433.