A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification

Stavros Degiannakis, Pamela Dent, Christos Floros

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)71-102
    Number of pages32
    JournalManchester School
    Volume82
    Issue number1
    DOIs
    Publication statusPublished - Jan 2014

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