Volatility extraction using the Kalman filter
AbstractThis paper focuses on the extraction of volatility of financial returns. The volatility process is modeled as a superposition of two autoregressive processes which represent the more persistent factor and the quickly mean-reverting factor. As the volatility is not observable, the logarithm of the daily high-low range is employed as its proxy. The estimation of parameters and volatility extraction are performed using a modified version of the Kalman filter which takes into account the finite sample distribution of the proxy.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies in its series Working Papers IES with number 2008/10.
Length: 15 pages
Date of creation: Jun 2008
Date of revision: Jun 2008
volatility; stochastic volatility models; Kalman filter; volatility proxy;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-06-27 (All new papers)
- NEP-ECM-2008-06-27 (Econometrics)
- NEP-ETS-2008-06-27 (Econometric Time Series)
- NEP-ORE-2008-06-27 (Operations Research)
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lenka Herrmannova).
If references are entirely missing, you can add them using this form.