A Monte Carlo Simulation Approach to Forecasting Multi‐Period Value‐At‐Risk and Expected Shortfall Using the FIGARCH‐skT Specification

32 Pages Posted: 18 Dec 2013

See all articles by Stavros Antonios Degiannakis

Stavros Antonios Degiannakis

Department of Economic and Regional Development, Panteion University of Political and Social Sciences

Pamela Dent

University of Portsmouth

Christos Floros

Technological Educational Institute of Crete

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Date Written: January 2014

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.

Suggested Citation

Degiannakis, Stavros Antonios and Dent, Pamela and Floros, Christos, A Monte Carlo Simulation Approach to Forecasting Multi‐Period Value‐At‐Risk and Expected Shortfall Using the FIGARCH‐skT Specification (January 2014). The Manchester School, Vol. 82, Issue 1, pp. 71-102, 2014, Available at SSRN: https://ssrn.com/abstract=2369104 or http://dx.doi.org/10.1111/manc.12001

Stavros Antonios Degiannakis

Department of Economic and Regional Development, Panteion University of Political and Social Sciences ( email )

136 Sygrou
Athens
Greece

Pamela Dent

University of Portsmouth

No Address Available
United Kingdom

Christos Floros

Technological Educational Institute of Crete ( email )

Department of Accounting & Finance
School of Management & Economics
Heraklion, Crete GR 71004
Greece

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