Type: Contribution to book
Forecasting Volatility Using High-Frequency Data
Michael P. CLEMENTS and David F. HENDRY (eds), The Oxford Handbook of Economic Forecasting, Oxford, Oxford University Press, 2011, Oxford Handbooks, 525-556
HANSEN, Peter Reinhard, LUNDE, Asger, Forecasting Volatility Using High-Frequency Data, in Michael P. CLEMENTS and David F. HENDRY (eds), The Oxford Handbook of Economic Forecasting, Oxford, Oxford University Press, 2011, Oxford Handbooks, 525-556 - https://hdl.handle.net/1814/26008
Retrieved from Cadmus, EUI Research Repository
This article focuses on some aspects of high-frequency data and their use in volatility forecasting. High-frequency data can be used to construct volatility forecasts. The article reviews two leading approaches to this. One approach is the reduced-form forecast, where the forecast is constructed from a time series model for realized measures, or a simple regression-based approach such as the heterogeneous autoregressive model. The other is based on more traditional discrete-time volatility models that include a modeling of returns. Such models can be generalized to utilize information provided by realized measures. The article also discusses how volatility forecasts, produced by complex volatility models, can benefit from high-frequency data in an indirect manner, through the use of realized measures to facilitate and improve the estimation of complex models.
Cadmus permanent link: https://hdl.handle.net/1814/26008
Full-text via DOI: 10.1093/oxfordhb/9780195398649.013.0020; 10.1093/oxfordhb/9780195398649.001.0001
Files associated with this item
There are no files associated with this item.