SEARCH

SEARCH BY CITATION

The paper provides a methodological contribution to the multi-step Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting through a new adaptation of the Monte Carlo simulation approach for forecasting multi-period volatility to a Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity (FIGARCH) framework for leptokurtic and asymmetrically distributed portfolio returns. Accounting for long memory within the conditional variance process with skewed Student-t (skT) conditionally distributed innovations, accurate 95 per cent and 99 per cent VaR and ES forecasts are calculated for multi-period time horizons. The results show that the FIGARCH-skT model has a superior multi-period VaR and ES forecasting performance.