Forecasting Value-at-Risk and Expected Shortfall Using Fractionally Integrated Models of Conditional Volatility: International Evidence

MPRA Paper No. 80433

International Review of Financial Analysis No. 27 (2013)

34 Pages Posted: 27 Oct 2018

See all articles by Stavros Antonios Degiannakis

Stavros Antonios Degiannakis

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

Christos Floros

Technological Educational Institute of Crete

Pamela Dent

University of Portsmouth

Date Written: 2013

Abstract

The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period Value-at-Risk (VaR) and Expected Shortfall (ES) across 20 stock indices worldwide. The dataset is comprised of daily data covering the period from 1989 to 2009. The research addresses the question of whether or not accounting for long memory in the conditional variance specification improves the accuracy of the VaR and ES forecasts produced, particularly for longer time horizons. Accounting for fractional integration in the conditional variance model does not appear to improve the accuracy of the VaR forecasts for the 1-dayahead,

10-day-ahead and 20-day-ahead forecasting horizons relative to the short memory GARCH specification. Additionally, the results suggest that underestimation of the true VaR figure becomes less prevalent as the forecasting horizon increases. Furthermore, the GARCH model has a lower quadratic loss between actual returns and ES forecasts, for the majority of the indices considered for the 10-day and 20-day forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve the VaR and ES forecasting accuracy, even for longer forecasting horizons. Finally, the rolling-sampled estimated FIGARCH parameters change less smoothly over time compared to the GARCH models. Hence, the parameters' time-variant characteristic cannot be entirely due to the news information arrival process of the market; a portion must be due to the FIGARCH modelling process itself.

Keywords: Expected Shortfall, Long Memory, Multi-Period Forecasting, Value-at-Risk, Volatility Forecasting

JEL Classification: G17, G15, C15, C32, C53

Suggested Citation

Degiannakis, Stavros Antonios and Floros, Christos and Dent, Pamela, Forecasting Value-at-Risk and Expected Shortfall Using Fractionally Integrated Models of Conditional Volatility: International Evidence (2013). MPRA Paper No. 80433, International Review of Financial Analysis No. 27 (2013), Available at SSRN: https://ssrn.com/abstract=3259843

Stavros Antonios Degiannakis (Contact Author)

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

136 Sygrou
Athens
Greece

Christos Floros

Technological Educational Institute of Crete ( email )

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

Pamela Dent

University of Portsmouth

No Address Available
United Kingdom

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