Analyzing Volatility Spillovers with the Multivariate FIGARCH Modeling: A case study of Tehran and Dubai stock index

seyed babak ebrahimi¹ Maryam Nezhad Afrasiabi²

1) aDepartment of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
2) aDepartment of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran

IOI Code : XYAC-ZCADC
Publication : 3rd.International Conference on Modern Research’s in Management,Economics & Accounting(3meaconf.com)
Abstract :
    The expansion of Globalization not only affects developed countries’ financial markets, but also the markets in developing countries. This condition causes investors who diversify their asset portfolio in foreign markets, pay serious attention to links between stock markets. This fact implies that there is an equilibrium relation between financial markets. Most of these markets have long-run memory characteristic which should be considered in modeling and estimation. Long memory in asset returns and volatilities is a new research area, both in theoretical and empirical modeling of high-frequency financial time series. The present paper aims to extend Constant Conditional Correlation (CCC) multivariate GARCH models to take into account the presence of long memory in daily financial time series. The extended model is more adaptable to real-world conditions, and it takes the long memory effect into account and estimates it throughout the estimation process. In order to justify the applicability of the proposed model, we investigated volatility spillover among the Tehran stock index, the Dubai stock index, and the global oil-price index using the MFIGARCH model covering the period from December 5, 2006 to January 30, 2012. The results of the estimation by different models generally showed volatility spillover from the global oil market to the Tehran and Dubai markets. Significant volatility spillover from the Dubai market to Tehran was observed as well, while no spillover effect was observed in the reverse direction.

Keywords : volatility spillovers multivariate figarch long memory eccc model high frequency