Stock Index Volatility Forecasting with High Frequency Data
AbstractThe increasing availability of financial market data at intraday frequencies has not only led to the development of improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in conjunction with a variety of volatility models for returns on the Standard & Poor's 100 stock index. We consider two so-calIed realised volatility models in which the cumulative squared intraday returns are modelled directly. We adopt an unobserved components model where actual volatility is modelled as an autore-gressive moving average process and an autoregressive fractionally integrated moving average model which allows for long memory in the logarithms of realised volatility. We compare the predictive abilities of these realised vola-tility models with those of daily time-varying volatility models, such as Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include the intraday volatility measure. For forecasting horizons ranging from one day to one week the most accurate out-of-sample volatility forecasts are obtained with the realised volatility and the extended SV models; all these models contain in-formation inherent in the high frequency returns. In the absence of the intraday volatility information, we find that the SV model outperforms the GARCH model.
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Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 02-068/4.
Date of creation: 28 Jun 2002
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ARFIMA; Financial market volatility; GARCH; Realised volatility; Stochastic volatility; Stock index returns; Unobserved ARMA component;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
This paper has been announced in the following NEP Reports:
- NEP-ALL-2002-12-02 (All new papers)
- NEP-CFN-2002-12-02 (Corporate Finance)
- NEP-ETS-2002-12-02 (Econometric Time Series)
- NEP-FMK-2002-12-02 (Financial Markets)
- NEP-RMG-2002-12-02 (Risk Management)
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- Georgios Chortareas & John Nankervis & Ying Jiang, 2007. "Forecasting Exchange Rate Volatility with High Frequency Data: Is the Euro Different?," Money Macro and Finance (MMF) Research Group Conference 2006 79, Money Macro and Finance Research Group.
- Chortareas, Georgios & Jiang, Ying & Nankervis, John. C., 2011. "Forecasting exchange rate volatility using high-frequency data: Is the euro different?," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1089-1107, October.
- Marshall, Ben R. & Cahan, Rochester H. & Cahan, Jared M., 2008. "Does intraday technical analysis in the U.S. equity market have value?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 199-210, March.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2003.
"Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility,"
PIER Working Paper Archive
03-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Sep 2003.
- Andersen, Torben G. & Bollerslev, Tim & Francis X. Diebold,, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," CFS Working Paper Series 2003/35, Center for Financial Studies (CFS).
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