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.
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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)
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