Forecasting Value-at-Risk and Expected Shortfall Using Fractionally Integrated Models of Conditional Volatility : International Evidence
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
Year of publication: |
2018
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Authors: | Degiannakis, Stavros Antonios |
Other Persons: | Floros, Christos (contributor) ; Dent, Pamela (contributor) |
Publisher: |
[2018]: [S.l.] : SSRN |
Subject: | Schätzung | Estimation | Volatilität | Volatility | Risikomaß | Risk measure | ARCH-Modell | ARCH model | Prognoseverfahren | Forecasting model | Zeitreihenanalyse | Time series analysis | Deutschland | Germany | Welt | World | Kapitaleinkommen | Capital income | Statistische Verteilung | Statistical distribution |
Saved in:
freely available
Extent: | 1 Online-Ressource (34 p) |
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Type of publication: | Book / Working Paper |
Language: | English |
Notes: | In: MPRA Paper No. 80433 Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments 2013 erstellt |
Classification: | G17 - Financial Forecasting ; G15 - International Financial Markets ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C32 - Time-Series Models ; C53 - Forecasting and Other Model Applications |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012910119