Authors 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 o...Expand abstract
- Host title:
- State space and unobserved components models: Theory and Applications
- Local pid:
Measuring and forecasting financial variability using realised variance.
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