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In this paper, we investigate the adequacy of the fractionally integrated asymmetric power model to measure value at risk in the United Arab Emirates stock exchanges. Our empirical results show that the accuracy of the model is improved when value at risk is computed using innovations modeled as skewed Student-t distribution. Including a long memory in the conditional volatility process would also improve the results. We conclude that, the modeling of asymmetry, fat tails and long memory have potentially important implications for risk assessment, and hedging strategies in the UAE stock exchanges.

Modeling and Forecasting Value-at-Risk in the UAE Stock Markets: The Role of Long Memory, Fat Tails and Asymmetries in Return Innovations