Robust M-estimation of multivariate FIGARCH models for handling volatility transmission: A case study of Iran, United Arab Emirates and the global oil price index

The stochastic nature of price volatility, as an important issue in stock markets, significantly affects decision makers' decisions. in this paper, a new multivariate fractionally integrated generalized autoregressive conditional heteroscedasticity (mvfigarch) model is proposed. being more comprehensive, in comparison with models in the literature, the proposed model considers the long term parameter, which is estimated simultaneously with other parameters. a well-known method of mvfigarch estimation is the gaussian quasi-maximum likelihood method. the gaussian quasi-maximum likelihood estimator of the mvfigarch model is known to be sensitive to data outliers. to correct this vulnerability, robust m-estimators are introduced for mvfigarch models. volatility models with bounded innovation propagation properties are introduced to increase the robustness of the estimations. the applicability of the proposed model is justified by the volatility transmission between the tehran stock index, the dubai stock index and the global oil price index between december 5th, 2006 to january 30th, 2012, and is investigated using the mvfigarch model. the result of estimation in different models generally shows the volatility transmission from the global oil market to tehran and dubai markets. the volatility transmission from the dubai to tehran market was meaningfully observed as well. however, the effect of transmission was not observed in the reverse direction.