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

Stavros Degiannakis, Pamela Dent and Christos Floros

MPRA Paper from University Library of Munich, Germany

Abstract: In financial literature, Value-at-Risk (VaR) and Expected Shortfall (ES) modelling is focused on producing 1-step ahead conditional variance forecasts. The present paper provides a methodological contribution to the multi-step VaR and ES forecasting through a new adaptation of the Monte Carlo simulation approach for forecasting multi-period volatility to a fractionally integrated GARCH 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% and 99% 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.

Keywords: Expected Shortfall; FIGARCH; Forecasting; stock indices; skewed Student-t; Volatility; Long Memory; Value-at-Risk; VaR. (search for similar items in EconPapers)
JEL-codes: C15 C32 C53 G15 G17 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (9)

Published in The Manchester School 82.1(2014): pp. 71-102

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Journal Article: A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification (2014)
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