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...
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MDPI AG
2020-02-01
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| Series: | Risks |
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| Online Access: | https://www.mdpi.com/2227-9091/8/1/12 |
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doaj-35b71bd0e40442b0be7710b3c1c4f7bb2020-11-25T01:27:38ZengMDPI AGRisks2227-90912020-02-01811210.3390/risks8010012risks8010012Do We Need Stochastic Volatility and Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns on FTSEDavid E. Allen0Michael McAleer1School of Mathematics and Statistics, University of Sydney, Sydney 2006, AustraliaDepartment of Finance, Asia University, Taichung 41354, TaiwanThe 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.https://www.mdpi.com/2227-9091/8/1/12stochastic volatilitygarch (1,1)ftserv 5 minhar modeldemeaned daily squared returns. |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
David E. Allen Michael McAleer |
| spellingShingle |
David E. Allen Michael McAleer Do We Need Stochastic Volatility and Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns on FTSE Risks stochastic volatility garch (1,1) ftse rv 5 min har model demeaned daily squared returns. |
| author_facet |
David E. Allen Michael McAleer |
| author_sort |
David E. Allen |
| title |
Do We Need Stochastic Volatility and Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns on FTSE |
| title_short |
Do We Need Stochastic Volatility and Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns on FTSE |
| title_full |
Do We Need Stochastic Volatility and Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns on FTSE |
| title_fullStr |
Do We Need Stochastic Volatility and Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns on FTSE |
| title_full_unstemmed |
Do We Need Stochastic Volatility and Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns on FTSE |
| title_sort |
do we need stochastic volatility and generalised autoregressive conditional heteroscedasticity? comparing squared end-of-day returns on ftse |
| publisher |
MDPI AG |
| series |
Risks |
| issn |
2227-9091 |
| publishDate |
2020-02-01 |
| description |
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. |
| topic |
stochastic volatility garch (1,1) ftse rv 5 min har model demeaned daily squared returns. |
| url |
https://www.mdpi.com/2227-9091/8/1/12 |
| work_keys_str_mv |
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| _version_ |
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