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Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model

Adnen Ben Nasr et al.

Applied Financial Economics2014https://doi.org/10.1080/09603107.2014.920476article
AJG 2ABDC B
Weight
0.64

Abstract

Appropriate modelling of the process of volatility has implications for portfolio
\nselection, the pricing of derivative securities and risk management. Further, a
\nlarge body of research has suggested that both long memory and structural
\nchanges simultaneously characterize the structure of financial returns volatility.
\nGiven this, in this article, we aim to model conditional volatility of the returns of
\nthe Dow Jones Islamic Market World Index (DJIM), interest on which has come
\nto the fore following the need for renovation of the conventional financial system,
\nin the wake of the recent global financial crisis. To model the conditional volatility
\nof the DJIM returns, accounting for both long memory and structural changes, we
\nallow the parameters in the conditional variance equation of the fractionally
\nintegrated generalized autoregressive conditional heteroscedasticity
\n(FIGARCH) model to be time dependent, such that the parameters evolve
\nsmoothly over time based on a logistic smooth transition function, yielding a
\nfractionally integrated time-varying generalized autoregressive conditional heteroscedasticity
\n(FITVGARCH) model. Our results show that, in terms of model
\ndiagnostics and information criteria, as well as, portfolio allocation, the
\nFITVGARCH model performs better than the FIGARCH model in explaining
\nconditional volatility of the DJIM returns, thus, highlighting the need to model
\nsimultaneously long memory and structural changes in the volatility process of
\nasset returns.

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https://doi.org/https://doi.org/10.1080/09603107.2014.920476

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@article{adnen2014,
  title        = {{Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model}},
  author       = {Adnen Ben Nasr et al.},
  journal      = {Applied Financial Economics},
  year         = {2014},
  doi          = {https://doi.org/https://doi.org/10.1080/09603107.2014.920476},
}

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Evidence weight

0.64

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.74 × 0.4 = 0.29
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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