Template-type: ReDIF-Paper 1.0
Author-Name: Ole E. Barndorff-Nielsen
Author-Email: oebn@mi.aau.dk
Author-Workplace-Name:The Centre for Mathematical Physics and Stochastics (MaPhySto), University of Aarhus, Denmark
Author-Name: Bent Nielsen 
Author-Email:bent.nielsen@nuf.ox.ac.uk 
Author-Workplace-Name: Nuffield College, Unviersity of Oxford, Oxford, UK
Author-Name: Neil Shephard
Author-Email:neil.shephard@nuf.ox.ac.uk 
Author-Workplace-Name: Nuffield College, Unviersity of Oxford, Oxford, UK
Author-Name: Carla Ysusi
Author-Email:ysusi@stats.ox.ac.uk 
Author-Workplace-Name: Dept of Statistics, Unviersity of Oxford, Oxford, UK
Title: Measuring and forecasting financial variability using realised variance with and without a model
Abstract:
 We use high frequency financial data to proxy, via the realised variance,
 each day's financial variability. Based on a semiparametric stochastic
 volatility process, a limit theory shows you can represent the proxy as a
 true underlying variability plus some measurement noise with known
 characteristics. Hence filtering, smoothing and forecasting ideas can be
 used to improve our estimates of variability by exploiting the time series
 structure of the realised variances. This can be carried out based on a
 model or without a model. A comparison is made between these two methods.
Keywords:Kalman filter; Mixed Gaussian limit; OU process; Quadratic variation; Realised variance; Realised volatility; Square root process; Stochastic volatility.
Length:32 pages
Creation-Date: 2002-10-07
Number:2002-W21
File-URL:http://www.nuff.ox.ac.uk/economics/papers/2002/w21/jim.pdf
File-Format: application/pdf
Handle: RePEc:nuf:econwp:0221