Measuring and forecasting financial variability using realised variance.
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 char...
| Hlavní autoři: | , , , |
|---|---|
| Další autoři: | |
| Médium: | Book section |
| Jazyk: | English |
| Vydáno: |
Cambridge University Press
2004
|
| Shrnutí: | 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 out based on a model or without a model. A comparison is made between these two methods. |
|---|