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A M onte C arlo Simulation Approach to Forecasting Multi‐period V alue‐at‐ R isk and E xpected S hortfall Using the FIGARCH ‐ skT Specification
Author(s) -
Degiannakis Stavros,
Dent Pamela,
Floros Christos
Publication year - 2014
Publication title -
the manchester school
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.361
H-Index - 42
eISSN - 1467-9957
pISSN - 1463-6786
DOI - 10.1111/manc.12001
Subject(s) - value at risk , econometrics , expected shortfall , autoregressive conditional heteroskedasticity , economics , volatility (finance) , heteroscedasticity , kurtosis , portfolio , cvar , monte carlo method , autoregressive model , conditional variance , financial economics , mathematics , statistics , risk management , finance
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.