Artikel
Do we need stochastic volatility and generalised autoregressive conditional heteroscedasticity? Comparing squared end-of-day returns on ftse
The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV) of FTSE sampled at 5 min intervals taken from the Oxford Man Realised Library. Both models demonstrated comparable performance and were correlated to a similar extent with RV estimates when measured by ordinary least squares (OLS). However, a crude variant of Corsi's (2009) Heterogeneous Autoregressive (HAR) model, applied to squared demeaned daily returns on FTSE, appeared to predict the daily RV of FTSE better than either of the two models. Quantile regressions suggest that all three methods capture tail behaviour similarly and adequately. This leads to the question of whether we need either of the two standard volatility models if the simple expedient of using lagged squared demeaned daily returns provides a better RV predictor, at least in the context of the sample.
- Language
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Englisch
- Bibliographic citation
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Journal: Risks ; ISSN: 2227-9091 ; Volume: 8 ; Year: 2020 ; Issue: 1 ; Pages: 1-20 ; Basel: MDPI
- Classification
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Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Asset Pricing; Trading Volume; Bond Interest Rates
- Subject
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demeaned daily squared returns
FTSE
GARCH (1,1)
HAR model
RV 5 min
stochastic volatility
- Event
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Geistige Schöpfung
- (who)
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Allen, David E.
McAleer, Michael
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2020
- DOI
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doi:10.3390/risks8010012
- Handle
- Last update
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10.03.2025, 11:46 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Allen, David E.
- McAleer, Michael
- MDPI
Time of origin
- 2020
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