Measuring and forecasting financial variability using realised variance with and without a model

Ole Barndorff-Nielsen, Bent Nielsen, Neil Shephard () and Carla Ysusi ()
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Carla Ysusi: Dept of Statistics, Unviersity of Oxford, Oxford, UK

No 2002-W21, Economics Papers from Economics Group, Nuffield College, University of Oxford

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. (search for similar items in EconPapers)
Pages: 32 pages
Date: 2002-10-07
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Citations: View citations in EconPapers (6)

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